Course Project Part 3Student NameDeVry University

Course Project Part 3
Student Name
DeVry University
BUSN 460 Senior Project
Dr. Michael Reitzel
Date
Contents
Executive Summary3
Section A: Business Concept3
Section B: Industry Analysis3
Section C: Regulation and Legal3
Section D: Competitive analysis4
Section E: Target Market and Segmentation4
Section F: Value Proposition4
Section G: Pricing Strategy5
Section H: Marketing Promotion Strategy5
Section I: Day-to-Day Operations5
Section J: Facilities and Equipment Plan5
Section K: Technology Plan6
Section L: Use of Funds6
Section M: Sales Forecast6
Section N: Breakeven7
Appendix8

References9
Course Project Part 3Executive Summary
This two-page summary of your plan is written last and should
be able to stand alone as a document on its own merits. Include
a clear and specific compelling Value Proposition with primary
research, a brief synopsis of each plan section, and brief
financial highlights. After reading this summary, the reader
should have a clear understanding of the specifics of your plan..
REPLACE INSTRUCTIONS WITH YOUR WORDS.Section A:
Business Concept
Describe in overview and in detail what you are offering to the
market. What does it "do"? What are the benefits to your
customers? How do the customers now accomplish the same
task? How is your approach better than the competition?
REPLACE INSTRUCTIONS WITH YOUR WORDS.Section B:
Industry Analysis
Research industry averages for profitability in your
marketplace. Use this information to determine the validity of
your own projections and make changes if necessary. REPLACE
INSTRUCTIONS WITH YOUR WORDS.Section C: Regulation
and Legal
Determine your location and business environment. Address all
legal, zoning, and licensing concerns your business will face.
Visit your state's Secretary of State website. What form of
business will you set up? Why? The level of detail required for
this section will depend on your type of location (virtual, retail,
warehouse, office, restaurant, etc.) and on your idea.
Demonstrate that you have completed your research. DON'T say
"We will obtain all of the appropriate permits"; instead,
summarize them. When you explain your form of business—
remember your audience. For example, if you select an S
corporation, explain your reasoning for that selection in the
context of your potential business, rather than providing the
definition of an S corporation. Address any pending regulations

which may have an impact on your business. REPLACE
INSTRUCTIONS WITH YOUR WORDS.Section D:
Competitive analysis
Describe the competitive landscape. Who are the key
competitors? What are their strengths and weaknesses? How
will you take share from them? How will they most likely try to
stop you if you are successful? Who are your indirect
competitors? What do they offer your prospects? Include a map
of their locations in your local area. REPLACE
INSTRUCTIONS WITH YOUR WORDS.Section E: Target
Market and Segmentation
Describe your market. Where is it? How big is it? What is the
growth rate? What are the unique features or dynamics of this
market? What causes people to buy? What are the demographics
and psychographics of your target customer? REPLACE
INSTRUCTIONS WITH YOUR WORDS.Section F: Value
Proposition
Specific evidence that people will buy your product or service.
What is your "hook?" Describe your primary research, and
explain how the results validate the value of your product or
service to your target audience. Primary market research is the
key to this evidence. Prove that if you make this investment,
customers will buy what you are selling. What is your
competitive edge? REPLACE INSTRUCTIONS WITH YOUR
WORDS.Section G: Pricing Strategy
Describe your pricing strategy and specific prices. How did you
arrive at these prices? What are competitive prices? Why are
yours different? How do your prices relate to costs and your
development investment? REPLACE INSTRUCTIONS WITH
YOUR WORDS.Section H: Marketing Promotion Strategy
Describe the role, the strategy, and the execution of your total
communications plan. What is your message? What are your
specific communication vehicles, such as advertising, literature,
promotion, the Internet? What type of scheduling or timing will
you use? Show your budget by year and type of expense.
REPLACE INSTRUCTIONS WITH YOUR WORDS.Section I:

Day-to-Day Operations
Describe how your business will "operate." If you make a
product, describe the production. If you offer a service, describe
each step of that service. If you are a retailer, location, product
mix, and suppliers are important. Think through your business'
daily operation and explain it in detail. Then, think about who,
what, and how each of those steps will happen. Realistically,
how much of this can one person do? Strategically, how will
you plan for growth? REPLACE INSTRUCTIONS WITH YOUR
WORDS.Section J: Facilities and Equipment Plan
Describe and cost your capital assets, such as production lines,
office equipment, and buildings. If you plan to have a physical
location, include a floor plan if possible. Address your build-
out strategy. Are you leasing a location that meets your specs
(fairly unusual); if not, include a build-out plan with high-level
milestones, dates, and costs? What are your startup timelines?
Expansion timelines? REPLACE INSTRUCTIONS WITH
YOUR WORDS.Section K: Technology Plan
Describe your company's IT needs and how much they will cost
and how you will implement. Will you have a web presence, and
if so, what type of functionality will it include? If you need a
particular software program, explain its function. Will you need
licenses for each employee? Will you handle your IT
requirements with "in-house" or outsource to IT consultants—
explain your decision. REPLACE INSTRUCTIONS WITH
YOUR WORDS.Section L: Use of Funds
Describe how you plan to use the startup requirements in detail
providing a start-up budget which includes all initial capital
expenditures, build-out and start-up expenses. The details must
be realistic and well researched. Data that does not make sense
will cost you points. In other words, if you are starting a
restaurant and your remodeling startup costs are $5,000, you
would be penalized, since that amount is unrealistic. REPLACE
INSTRUCTIONS WITH YOUR WORDS.Section M: Sales
Forecast
Create your 5-year forecast (See lecture for details). Units,

dollars and assumptions are critical. Create the sales forecast in
a narrative. These may be based on the optional worksheets.
(Remember, you don't submit the spreadsheet created using the
course financial software, so restate important numbers in the
form of charts, tables or excerpts from the SalesProj tab in your
spreadsheet.) Your forecast is the description of the units you
plan to sell, the services (amount of them) you plan to provide,
and your growth projections of these numbers. Document all
assumptions, and provide external source information for all
assertions. REPLACE INSTRUCTIONS WITH YOUR
WORDS.Section N: Breakeven
Include a graphical representation that shows when your
company will start making a profit. REPLACE INSTRUCTIONS
WITH YOUR WORDS.
Appendix
You must use primary research as described in the Marketing
section. Your Appendix must include evidence of that research.
Examples might include survey responses, or interview notes.
Faculty will confirm primary research—failure to provide
evidence is an automatic 50-point deduction. Faculty, type in 0
points for the entire section if no primary research is provided
in the final plan.
References
Abrams, R., (2014). Successful Business Plan: Secrets and
Strategies (6th Edition). PlanningShop (US).
Vol.:(0123456789)
Social Indicators Research (2020) 147:501–516

https://doi.org/10.1007/s11205-019-02159-z
1 3
O R I G I N A L R E S E A R C H
Refining the Monetary Poverty Indicators Under a Join
Income‑ Consumption Statistical Approach: An Application
to Spain Based on Empirical Data
Antonio M. Salcedo1 · Gregorio Izquierdo Llanes2
Accepted: 13 July 2019 / Published online: 17 July 2019
© Springer Nature B.V. 2019
Abstract
In the European Union poverty has been measured indirectly in
a one-dimensional way
from a perspective based on disposable income. This classical
approach has certain limita-
tions when representing such a complex phenomenon by means
of a single variable, reach-
ing sometimes a modest association with regard to other direct
poverty measurements such
as severe material deprivation rate. In this article we study the
measurement of monetary
poverty from a two-dimensional point of view favouring a
perspective of complementarity
rather than one of substitutability. The joint analysis of the
monetary income and consump-
tion distribution makes it possible to identify different
association patterns between these
two variables for individuals located on one side or the other of
the respective poverty
thresholds. Expenditure on housing that is a determining factor
in lower-income house-

holds and imputed rents that would be paid by the owner
household of a dwelling, allow us
to calculate an at-risk-of poverty rate which refines the link
with material poverty in both
temporal and spatial dimensions.
Keywords At-risk-of poverty · Material deprivation ·
Disposable income · Residual
income · Sensitivity · Spain
1 Introduction
In recent decades monetary poverty has been measured,
specifically, by means of the
poverty risk rate based on disposable income (Atkinson et al.
2017). This paradigm,
generally accepted in the European Union (EU), has been
reconsidered since the recent
economic crisis, given that the indicators of severe material
deprivation have shown
more variation than the classical indicator of at-risk-of poverty,
which in turn has led to
* Antonio M. Salcedo
[email protected]
Gregorio Izquierdo Llanes
[email protected]
1 Universidad Complutense de Madrid (UCM), Madrid, Spain
2 Universidad Nacional de Educación a Distancia (UNED),
Madrid, Spain
502 A. M. Salcedo, G. Izquierdo Llanes
1 3

a lower degree of association between them. One way to solve
this possible dysfunction
is to understand that the relationship between income and
consumption has been modi-
fied by the existence of savings and/or by variations in debt
service. This would lead
to the need to measure the risk of poverty not only from the
perspective of monetary
income, but also from that of monetary consumption (Meyer and
Sullivan 2017). Both
visions of poverty have been accepted as valid by the UNECE in
its recent Manual for
the harmonized measurement of poverty (UNECE 2017).
In this sense, when applying the classical one-dimensional
poverty measurement
model based on income, some researchers have noted the
existence of a relative modest
association between the risk of poverty and material poverty
(Notten and Guio 2018),
when the latter is measured in terms of the proportion of
individuals in a situation of
severe material deprivation, taking into account both their
degree of correlation (Notten
2016) and the intersection between the two subpopulations
(Fusco et al. 2010).
Thus, if we focus specifically on the data for a selection of EU
countries in 2016
included in Table 1, we see that the sensitivity of the risk of
poverty with respect to
severe material deprivation stands at only 36.4% in the case of
Finland; in other words,
approximately one in three of those in a situation of material
poverty is at risk of mon-

etary poverty but the other two material poor are out of risk of
monetary poverty. The
corresponding figure is similar in the case of Hungary (38.9%),
while it increases for
Italy (44.2%) and the United Kingdom (46.7%). In France
around one out of two of
those in a situation of material poverty is at risk of monetary
poverty (a sensitivity of
51.1%), while the results indicate higher values in the cases of
Spain (69.0%) and Ger-
many (70.3%), the latter being the highest value of all the EU
countries.
An additional debate exists regarding whether the monetary
poverty paradigm, given
that it is a one-dimensional measurement system, could be
improved by incorporating
other dimensions (Alkire et al. 2015) in order to better
represent such a complex phe-
nomenon (Serafino and Tonkin 2017). A join statistical
approach has been adopted at
European level through the so-called Vienna memorandum on
Income, Consumption
and Wealth statistics, endorsed in 2016, which is consistent
with the framework advo-
cated by the Organisation for Economic Co-operation and
Development (OECD 2013).
At micro level, the memorandum promotes additional
development and coordination of
Table 1 Intersection and sensitivity of the at-risk-of-poverty
and severe material deprivation rate, year
2016. Source: Prepared by the authors based on Eurostat
database—intersections of Europe 2020 poverty
target indicators

Country At-risk-of-poverty
rate (%)
(a)
Severe material depriva-
tion rate (%)
(b)
Intersection of (a)
and (b) (%)
(c)
Sensitivity (%)
(c/b)
Finland 11.7 2.2 0.8 36.4
Hungary 14.4 16.2 6.3 38.9
UK 15.9 5.2 2.3 44.2
Italy 20.6 12.0 5.6 46.7
France 13.7 4.5 2.3 51.1
Spain 22.3 5.8 4.0 69.0
Germany 16.5 3.7 2.6 70.3
503Refining the Monetary Poverty Indicators Under a Join…
1 3
the main statistical data sources, especially EU-SILC,
Household Budget Survey (HBS)
and Household Finance and Consumption Survey (HFCS).
Concerning the integration of those variables, it is also worth
noting that net wealth
conditions the need for savings or the direct and indirect

financing of consumption; this
could explain the discrepancies between the income and
consumption of some individuals.
In any case, wealth, insofar as it is positive or negative,
involves returns or debt service
which affect income and/or consumption. In particular some
authors have considered hous-
ing expenditure as an explanatory factor of some situation of
poverty risk (Yang 2018).
The so-called income-ratio is a mainstream in the financial
economy to measure accessibil-
ity, based on linking the information of defaults to indicators
constructed from the relative
ratio between housing expenditure and household income
(Bramley 2012), whose main
weakness is that non-housing expenditures must represent a
minimum proportion, which
is not very applicable to households with incomes far from the
average (Haffner and Hey-
len 2011). But because of its potential applicability to the
measurement of poverty, the
alternative accessibility paradigm called residual income is
particularly interesting (Stone
2006), which is based on quantifying the absolute level of the
difference between income
and housing expenses, relating this difference with what is
estimated as a fair standard of
living. Like the economy of poverty, the residual income
approach has the main difficulty
of quantifying this fair standard of living since it is different for
each temporal and spatial
reality (Li 2015).
Based on all previous introductory considerations, we attempt
an initial approach to a
two-dimensional model using the joint distribution of monetary

income and consumption
which, applied to the case of Spain, will provide the basis for
the construction of an indi-
rect estimator of monetary poverty which represents a
refinement of the classical poverty
rate.
2 Data and Methods
The classical approach to the measurement of monetary poverty
has considered as at-risk-
of-poverty those individuals whose disposable income in a year
t is to be found on the left
of what is known as the poverty line (Ravallion and Lokshin
2006). Thus, the monetary
poverty risk rate is given by the proportion of individuals whose
equivalent disposable
income is below the poverty threshold (Lelkes and Gasior
2018). A percentage (p) of the
median (Mdn) of the equivalent disposable income is normally
used to define this poverty
threshold. This percentage is conventionally set at p = 60% in
the case of the EU (Atkin-
son and Marlier 2010) even though the UNECE or the OECD
recommend using values of
p = 50% for international comparisons (OECD 2016). Methods
of selection of p depending
on their sensitivity and specificity with respect to material
poverty have been analysed by
some authors in order to draw optimal poverty lines (Salcedo
and Izquierdo Llanes 2018).
Thus, if we denote the equivalent disposable income of the
individuals of a country as
Yd, the poverty line or threshold based on a percentage p of its
median will be given by

yline,p, calculated as follows:
The above calculation can be used for any other monetary
variable, either income (Y) or
consumption (C), by simply replacing the new income or
consumption variable in Eq. (1).
(1)yline,p = p% ∗ Mdn
(
Yd
)
1 3
Thus, for the purpose of this article, we will denote the poverty
threshold of equivalent
monetary consumption for p = 60% as cline,60.
At this point, and before extending a one-dimensional model to
a two-dimensional
model, let us consider the following proposition: “Let N be the
total number of individuals
in a country or region under study. Then the at-risk-of-poverty
rate with p = 60%, which we
denote in this article as Arop.RYd,60, is the value of the
distribution function of the equiva-
lent disposable income (FYd) evaluated on the poverty
threshold (yline,60)”. Given that Arop.
RYd,60 represents the proportion of individuals with an
equivalent income below the poverty
line with p = 60%, then:

Figure 1 shows the cumulative distribution function and the
poverty risk rate of Spain
calculated for the year 2017. This rate was 21.6% or, in other
words, the risk of poverty rate
Arop.RYd,60 is located in percentile 21.6 of the distribution
function of Yd. In case of using
p = 50% the monetary poverty rate is 15.7%.
Based on the aforementioned proposition, when considering a
two-dimensional income-
consumption variable we can immediately define a two-
dimensional poverty risk rate as
the value of the two-dimensional distribution function FY,C
evaluated at the centroid deter-
mined by the respective one-dimensional poverty thresholds, as
follows:
From a methodological point of view, in order to validate the
results of this model exter-
nally, we will use the degree of association obtained by means
of the different correlation
coefficients with the rate of population suffering severe
material deprivation, a direct meas-
ure of poverty (Chzhen et al. 2016). In the EU, a person who
cannot afford at least four of
the following nine items (Rajmil et al. 2015) is considered to
be in a situation of severe
material deprivation (Ayllón and Gábos 2017): to pay rent or
utility bills; to keep home
adequately warm; to face unexpected expenses; to eat meat, fish
or a protein equivalent
every second day; a week holiday away from home; a car; a
washing machine; a colour TV;
a telephone. The results of this indicator have been analysed by
various authors with a view

to suggesting possible improvements (Guio et al. 2016). The
parameters of sensitivity,
(2)
Arop.RYd,60 =
number of individuals with Yd ≤ yline,60
N
= P
(
Yd ≤ yline,60
)
= FYd
(
yline,60
)
(3)Arop.RYC,60 = FY,C
(
yline,60, cline,60
)
Fig. 1 Cumulative distribution
function (FYd), poverty line
(yline,60) and at-risk-of poverty
rates (Arop.RY,60 and Arop.RY,50)
in Spain, year 2017. Source:
Prepared by the authors based
on the Living Conditions Survey
microdata

1 3
specificity and accuracy, often used in the estimation of results
using ROC curves (Fawcett
2006) are also applied to perform an internal analysis of poverty
at the microdata level.
The study uses empirical information from two main sources:
the Household Budget Sur-
vey (HBS) and the Living Conditions Survey (LCS) that is
Eurostat’s equivalent of the EU-
SILC. All the anonymized microdata files can be downloaded
free on the website http://
www.ine.es/en/prody ser/micro datos _en.htm.
3 An Application to Spain
3.1 The Joint Distribution of Monetary Income
and Consumption
We begin with the study of the joint distribution of the
monetary income and consumption of
Spanish households. Figure 2 shows the two-dimensional
scatter diagram of these two vari-
ables at microdata level obtained from the HBS with 2017 as
reference year. This figure shows
the representation of the 2D density lines. In the upper part and
on the right, the marginal den-
sity functions of income and consumption are also shown. The
two poverty lines of net income
(yline,60) and monetary consumption (cline,60) calculated with
p = 60% have also been added.

The inclusion of the two poverty lines makes it possible to
visualize the two-dimensional
Area I: At risk of income and consumption poverty (10.5%).
Area II: At risk of income poverty but out of risk of
consumption poverty (10.8%).
Area III: At risk of consumption poverty but out of risk of
income poverty (8.8%).
Area IV: Out of risk of income and consumption poverty
(69.9%).
Fig. 2 2D-density scatter plot of equivalent net income and
monetary expenditure, year 2017. Source: Pre-
pared by the authors based on HBS microdata (2017)
1 3
centroid (yline,60,cline,60) as the intersection of the one-
dimensional income and consumption
poverty thresholds, respectively. This, in turn, means the
quadrant can be divided into four
clearly differentiated areas.
In area I, all individuals are below the two poverty thresholds
(yline,60,cline,60). Given that
everyone in this zone experiences low levels of both income and
consumption, a high degree
of correlation between the risk of poverty and the rate of the
population in severe material
deprivation would be expected. In area II, individuals have a
low level of income but their lev-
els of monetary expenditure are medium–high, since they are

located above the poverty line
for consumption (cline,60). This situation could be related to
the sale of household goods, the
reduction of previously accumulated savings, indebtedness,
family assistance or might even
suggest the existence of informal or illegal shadow economy
activities (Eurostat 2018). The
individuals in area III have a low level of monetary expenditure
but their income levels are
medium–high since they are located above the income poverty
line (yline,60). They could be
saving and/or facing debt service. It should be noted that low
levels of monetary consump-
tion could be significantly affected by the different price levels
(PPP) to be found in Spain’s
autonomous communities (Salcedo and Izquierdo Llanes 2017),
which could condition the
measurement of the risk of poverty. Finally, the individuals in
area IV have medium–high lev-
els of income and monetary spending; they are all located above
the two poverty lines. This
situation indicates that these individuals are not at risk of
poverty.
Given the existence of a high degree of association between
household income and
expenditure, it would be expected, a priori, that the percentage
of people at risk of income and
consumption poverty would be very high in relation to the total
population in one or another
risk. However, we observe that only 1 in 3 of those at risk of
income or consumption monetary
poverty (30.1%, total of areas I + II + III) is simultaneously at
risk of income and consumption
poverty (10.5%, area I); this seems to suggest an anomalous
situation in the one-dimensional

models of income or consumption poverty when these are
considered separately.
Table 2 shows the correlation coefficients obtained between the
proportion of people in a
situation of severe material deprivation and the monetary
poverty risk rates in Spain based on
EU-SILC and HBS data. The period analysed spans the years
2008 to 2017, which is espe-
cially significant since it covers the whole period affected by
the recent financial crisis. It can
be observed that the two-dimensional model offers a very high
degree of association, sur-
passing even the good results obtained from the one-
dimensional models, in particular the
standard used in the EU-SILC. Therefore, a two-dimension rate
based on low income and low
consumption could be a better monetary poverty indicator than
low income or low consump-
tion alone, which are the most prevalent approaches of relative
poverty at present.
The results of this table and the joint distribution income-
consumption suggest the possible
existence of an indicator, based on a linear combination of the
variables of income and con-
sumption, which could offer a better approximation to the
measurement of poverty than the
one-dimensional classical indicator based exclusively on
disposable income, as it is applied
in the European Union among others. Following an analysis of
the joint distribution of the
equivalent income and monetary consumption in Fig. 2 and the
poverty measurement results
presented in Table 2, we proceed to a principal components
analysis of the income and con-

sumption data for 2017, which provides us with the following
standardized linear equations:
It can be seen that the first principal component (PC1) provides
an eigenvector on
the diagonal of the first quadrant. In Table 3 we show the
cumulative proportion of total
(4)
{
PC1 ∶ 0.707 ∗ Y + 0.707 ∗ C
PC2 ∶ 0.707 ∗ Y − 0.707 ∗ C
1 3
variability explained by this component (76.93%), which can be
considered as significant
and indicates that most of the two-dimensional variability is
concentrated in this first com-
ponent, that is, along the straight line on which standardized
income and consumption are
equal.
The second principal component (PC2), meanwhile, explains
23.06% of the remain-
ing variability with a subtraction, indicating a contrast between
net income and monetary
expenditure; this could be interpreted as the different levels of
monetary savings of house-
holds. According to this second principal component, in the

case of simultaneously low
values of Y and C, the range of variation of savings (positive or
negative) is also low; this
in turn implies the existence of a low capacity of indebtedness
of households and could
result in situations of poverty and/or financial exclusion
(Krumer-Nevo et al. 2017) affect-
ing the financial well-being of households (Lee and Sabri
2017).
It should be pointed out, following on from the previous
reflection, that there is a wide
range of financial ratios for households calculated for different
purposes (Harness et al.
2008). The European Central Bank, for example, has considered
various consumption-
to-income ratios in the scope of the Household Finance and
Consumption Survey (ECB
2016). Besides, in the framework of the EU-SILC, a
transformation of disposable income
is also frequently used by adding the imputed rents from the
dwelling to the equivalent
Table 2 Direct and indirect poverty rates, period 2008–2017
Prepared by the authors based on the EU-SILC database (*) and
HBS microdata (**)
Year Direct poverty
measurement
(%)
Indirect poverty measurement (%)
One dimension Two dimensions

Severe material
deprivation rate*
Arop.RYd,60* Arop.RC,60** Arop.RYC,60**
2017 5.1 21.6 19.3 10.5
2016 5.8 22.3 19.0 10.3
2015 6.4 22.1 19.6 10.6
2014 7.1 22.2 19.1 10.7
2013 6.2 20.4 18.1 9.8
2012 5.8 20.8 18.2 9.1
2011 4.5 20.6 18.2 9.1
2010 4.9 20.7 18.6 8.8
2009 4.5 20.4 17.9 8.7
2008 3.6 19.8 17.8 8.3
Pearson corr. coef. 0.73 0.59 0.83
Spearman corr. coef. 0.68 0.61 0.88
Kendall corr. coef. 0.60 0.48 0.75
Table 3 Summary of principal
components analysis, year 2017.
Source: Prepared by the authors
based on HBS microdata
PC1 PC2
Standard deviation 1.2404 0.6792
Proportion of variance 0.7693 0.2306
Cumulative proportion 0.7693 1.0000
1 3

income (Törmälehto and Sauli 2013), in order to offer a
complementary measure of mon-
etary poverty; although imputed rents are not, by definition,
part of equivalent income, it
can be considered as an aggregate income in national
accounting terms (Eurostat 2013).
In this context and for the purpose of this article we denote as
Yid the variable disposable
income adding imputed rents and equivalised following the
usual procedures.
Given that housing is usually purchased using a loan and, if
income is not adjusted with
financial expenses this could have the perverse effect that
someone who bought a home
with a loan of 100%, and whose imputed income was dedicated
to servicing the loan,
would be considered to have a greater income than just before
buying the home, that coin-
cides with the temporary moment when that person did not pay
any mortgage although he
or she could be facing the payment of a rent (Attanasio et al.
2012). This possible dysfunc-
tion leads to the incorporation of the expenses related with
housing, mainly debt service
and rent, into the indicators used to calculate the at-risk-of-
poverty. In addition, in the case
at hand, the expression of the first principal component of the
joint distribution of income
and monetary consumption induces us to search for linear
combinations, in the form of
subtractions between income and consumption, in order to
obtain the greatest variability
possible.
Taking into account all of the above, we analyse the HBS to

identify the item of highest
monetary expenditure in the lowest income households, based
on the international classi-
fication COICOP (Berardi et al. 2017) which breaks down
household expenditure into the
following twelve groups: (1) Food and non-alcoholic beverages;
(2) Alcoholic beverages,
tobacco and narcotics; (3) Clothing and footwear; (4) Housing,
water, electricity, gas and
other fuels; (5) Furniture, household equipment and ordinary
expenses for the maintenance
of the dwelling; (6) Health; (7) Transport; (8) Communication;
(9) Leisure, performances
and culture; (10) Education; (11) Restaurants, cafés and hotels;
(12) Miscellaneous goods
and services.
Of these twelve groups, spending on group 4 (housing) is
clearly the largest of all
expenditure items in households with the lowest income.
Table 4 shows the proportion of
expenditure on housing (including rent, interest payments on
mortgages, water, electricity,
gas and other fuels) by income quintile in five European
countries in 2015. We can see that
the percentage of monetary expenditure associated with this
group is around 40% of total
expenditure for households in the first income quintile, while in
the case of households in
the top quintile this percentage decreases by between − 9.5 and
− 15.3 percentage points.
It is clear that, unlike other COICOP items such as alcoholic
beverages and tobacco,
leisure and culture or eating out, this item of expenditure is
obligatory for households and

its high proportion in the lower income quintile clearly
conditions the capacity to pay for
other fundamental goods or services; this could be related to
situations of severe material
deprivation in low-income households.
Table 4 Percentage of monetary
expenditure in housing, water,
electricity, gas by income
quintile (year 2015). Source:
Prepared by the authors based
on Eurostat database - Structure
of consumption expenditure by
income quintile and COICOP
consumption purpose
Country Income quintile Diff. (p.p.)
Q1 Q5
Bulgaria 39.7 28.6 − 11.1
Finland 39.1 27.0 − 12.1
Germany 43.3 28.0 − 15.3
Hungary 46.1 31.0 − 15.1
Spain 38.6 29.1 − 9.5
1 3
For all these reasons and based on the above results, we define
the equivalised income
characterized by expenditure on housing, which henceforth we
will call Ydc, as the disposa-

ble income of the household once the total expenditure on
housing has been deducted; this
latter figure is reflected in the EU-SILC at the microdata level
and it has been recently used
by Eurostat to calculate other poverty rates that differ from the
standard use of Eq. (1).
This variable has also commonalities with the concept of
residual income (Stone 2006).
Finally, the equivalised imputed income characterized by
expenditure on housing (Yidc) is
also defined analogously to Ydc but adding imputed rents to the
characterized income. In
the next section we investigate whether the characterized
income offers an improvement
over the classical poverty risk estimator based solely on
disposable income.
3.2 Refining the Classical Measurement of the Monetary
Poverty
To check the quality of the estimation of the monetary poverty
risk based on characterized
income, we will take the last available year (2017) as our
reference year and, using the
classical estimator based on Yd and with p = 60% of the
median, we will carry a compara-
tive study of the poverty rates based on the three income
variables previously presented
in this paper, that is, Yid, Ydc and Yidc, and also with p = 60%
of their respective medians
according to Eq. (1).
Firstly, we verify that the areas under the ROC curve (López-
Ratón et al. 2014) obtained
with the variables Yd, Yid, Ydc and Yidc in 2017 are 0.82,
0.84, 0.83 and 0.84, respectively. It

can be shown that the area under the ROC curve (AUC), which
takes values between 0.5
and 1.0, is equivalent to that of the Mann–Whitney test (Hand
and Till 2001). We can see
that in this case Yid and Yidc offer the highest values of the
AUC.
Our second test consists of an analysis -external and internal- of
the temporal dimen-
sion. Table 5 shows the proportion of individuals in a situation
of severe material depriva-
tion as well as the poverty risk rates obtained using the
variables Yd, Yid, Ydc and Yidc for the
decade 2008–2017.
Table 5 Severe material deprivation and at-risk-of poverty rates
(%) based on variables Yd, Yid, Ydc and Yidc.
Source: Prepared by the authors based on LCS microdata (2008–
2017)
Year SMD rate Arop.RYd,60 Arop.RYid,60 Arop.RYdc,60
Arop.RYidc,60
2017 5.1 21.6 19.7 25.6 22.6
2016 5.8 22.3 19.8 25.8 22.6
2015 6.4 22.1 19.5 25.4 22.8
2014 7.1 22.2 19.9 26.1 23.5
2013 6.2 20.4 18.7 24.5 22.5
2012 5.8 20.8 19.0 24.9 22.3
2011 4.5 20.6 17.8 24.6 21.6
2010 4.9 20.7 17.6 24.3 21.6
2009 4.5 20.4 17.3 24.0 21.2
2008 3.6 19.8 17.1 23.6 20.7
Pearson corr. coef. 0.73 0.82 0.78 0.94
Spearman corr. coef. 0.68 0.80 0.74 0.91
Kendall corr. coef. 0.60 0.66 0.61 0.81

1 3
It is observed that the monetary poverty rate derived from
disposable income by add-
ing imputed rents (Yid) is lower than the classical one, between
− 1.7 and − 3.1 per-
centage points. On the contrary, the disposable income
characterized by expenditure in
housing (Ydc) increased the rates from + 3.3 to + 4.1 percentage
points. The inclusion of
imputed rents in the characterized income (Yidc) offers more
similar rates than the classi-
cal indicator, with differences ranging from + 0.3 to + 2.1
percentage points. The corre-
lation coefficients obtained are very high in all cases, although
the variable Yidc offered
very high values (0.94, 0.91 and 0.81).
The situation is similar when an internal study—at micro
level—of the sensitivity,
specificity and accuracy of the four variables over time is
carried out. Table 6 shows
that, in all cases, the characterized income is more sensitive
than that of Yd, reaching
a maximum of 76.2% in 2016; this is an indication that the
intersection between the
risk of poverty rate and severe material deprivation is greater
with this variable. As far
as specificity is concerned, the highest values are obtained
when considering imputed
rents only (84.6% in 2008 and 2009), that is, this variable offers

the largest intersection
between individuals that are not materially poor and out of risk
of poverty, simultane-
ously. Finally, the accuracy of the variable Yid is again the
highest of the four cases
considered, with a maximum of 83.8% in 2008. This table also
shows that all sensitivity
results for Yidc are greater than those for the classical Yd with
p = 60%, reaching + 10.8
percentage points in 2011, while the specificity and accuracy
are rather similar, around
80% every year.
Finally, as a third test, we studied the spatial dimension,
focusing on the results cal-
culated for the seventeen Spanish autonomous communities at
the NUTS2 level with ref-
erence year 2017. In the internal analysis, Table 7 shows the
severe material deprivation
and poverty risk rates obtained from the equivalent disposable
income and the equiva-
lent characterized income for all regions. To simplify this
analysis, only the sensitivity
(Se.) of the poverty risk rate with respect to severe material
deprivation is used.
Table 6 Sensitivity, specificity and accuracy (%) with regard to
the severe material deprivation. Source:
Prepared by the authors based on LCS microdata (2008–2017)
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Sensitivity (Se.)
Yd,60 57.8 58.8 58.9 53.9 57.9 56.0 63,1 62.0 69.6 63.8
Yid,60 62.3 58.6 59.0 54.2 59.7 61.1 64.1 64.7 69.9 62.6
Ydc,60 70.1 67.1 66.3 63.6 67.7 65.5 72.0 72.3 76.2 70.4

Yidc,60 68.3 66.5 64.2 64.7 65.3 65.9 70.4 70.6 74.1 68.7
Specificity (Sp.)
Yd,60 81.6 81.4 81.3 80.9 81.5 82.0 80.9 80.6 80.6 80.7
Yid,60 84.6 84.6 84.5 83.9 83.5 84.1 83.5 83.6 83.3 82.6
Ydc,60 78.1 78.0 77.8 77.3 77.8 78.2 77.4 77.8 77.3 76.8
Yidc,60 81.1 80.9 80.6 80.5 80.4 80.4 80.0 80.4 80.5 79.9
Accuracy (Acc.)
Yd,60 80.7 80.4 80.2 79.7 80.1 80.4 79.6 79.4 79.9 79.8
Yid,60 83.8 83.4 83.3 82.6 82.2 82.7 82.1 82.4 82.5 81.6
Ydc,60 77.8 77.5 77.3 76.7 77.2 77.4 77.0 77.5 77.2 76.5
Yidc,60 80.7 80.2 79.8 79.8 79.5 79.5 79.4 79.8 80.2 79.3
1 3
The classical variable Yd offers low sensitivity in regions ES23
(Rioja) and ES21 (País
Vasco) of only 23.2% and 35.7%. The inclusion of imputed
rents Yid increases the sen-
sitivity in eight of the seventeen autonomous communities, is
unchanged in three and
reduces in six. All sensitivity values improve significantly when
the characterized equiva-
lent income is considered. It is also noteworthy that in the
autonomous communities ES13
(Cantabria) and ES24 (Aragón) the new variable reaches a
sensitivity of 100%; that is,
in these cases the maximum possible intersection is achieved.
Besides, ES30 (Madrid)
and ES43 (Extremadura) are the regions with highest and lowest
GDP per capita in Spain

respectively; they have a severe material deprivation rate rather
similar (5.4% and 5.6%
respectively, + 0.2 percentage points only) but the situation is
quite different when check-
ing the classical risk of monetary poverty (16.9% and 38.8%,
that is, + 21.9 percentage
points). After adding imputed rents the poverty rate doesn’t
change too much in Madrid
but in Extremadura the risk of poverty is reduced to 33.5%. If
deducing housing costs,
Madrid increases the monetary poverty to 22.7% and
Extremadura to 41.0%. The com-
bined effect of imputed rents and housing costs (Yidc) set the
risk of poverty in 20.8% in
Madrid and 33.7% in Extremadura, reducing the difference to +
12.9 percentage points. In
this last case it is remarkable that the sensitivity is also
increased to 71.4% in Madrid and
79.9% in Extremadura.
Regarding the analysis evaluated via different degrees of
association, in Fig. 3 it can be
observed that the correlation between poverty risk rates and
severe material deprivation by
regions is increased by using Yidc, with the coefficient of
determination rising from 0.39
Table 7 At risk of poverty rates and sensitivity (%) with regard
to the population on severe material dep-
rivation, year 2017 (highest sensitivity values in italics).
Source: Prepared by the authors based on LCS
microdata
NUTS 2 SMD rate Yd,60 Yid,60 Ydc,60 Yidc,60
Arop.R Se. Arop.R Se. Arop.R Se. Arop.R Se.

Total 5.1 21.6 63.8 19.7 62.6 25.6 70.4 22.6 68.7
ES11 Galicia 2.4 18.7 80.9 16.6 77.5 20.3 80.9 17.8 78.7
ES12 Asturias 3.5 12.6 78.4 12.3 78.0 15.7 88.4 14.9 83.3
ES13 Cantabria 2.2 17.6 84.5 13.0 84.5 21.9 100.0 18.8 100.0
ES21 País Vasco 3.7 9.7 35.7 8.6 40.7 14.0 50.7 11.4 58.6
ES22 Navarra 0.3 8.3 68.3 8.4 68.3 11.6 88.3 11.4 88.3
ES23 Rioja 2.9 9.7 23.2 11.2 30.4 16.2 64.0 14.2 64.0
ES24 Aragon 0.5 13.3 88.6 10.2 88.6 16.0 100.0 12.4 88.6
ES30 Madrid 5.4 16.9 67.8 16.6 61.4 22.7 74.9 20.8 71.4
ES41 C. León 1.0 15.4 52.4 14.1 57.4 20.2 76.3 16.6 76.3
ES42 C. Mancha 4.4 28.1 50.6 26.9 38.8 31.6 57.5 31.1 48.8
ES43 Extremad. 5.6 38.8 64.5 33.5 70.8 41.0 75.0 33.7 79.9
ES51 Cataluña 5.0 15.0 60.0 13.3 54.3 20.2 66.9 18.5 66.0
ES52 C. Valenc. 7.4 25.6 64.3 24.2 65.0 29.1 67.2 25.4 66.9
ES53 I. Balears 6.9 21.3 61.2 23.8 64.0 28.6 64.6 26.8 64.0
ES61 Andalucia 5.2 31.0 71.1 27.5 77.1 33.8 80.1 28.4 74.5
ES62 Murcia 6.2 30.1 67.9 25.9 69.8 35.3 77.3 27.9 73.8
ES70 Canarias 13.6 30.5 58.0 25.9 52.7 32.2 59.0 32.1 62.0
1 3
to 0.53, which means a greater proportion of variability which
can be explained using the
new variable. The Spearman and Kendall correlation
coefficients, meanwhile, also improve
from 0.69 and 0.51 with the classical poverty rate to 0.76 and
0.54 respectively with the
estimator based on Yidc.
To conclude the analysis of the spatial dimension, Table 8
shows the poverty rates by

degree of urbanisation. Severe material deprivation rate is
higher in very populated areas
(cities, 6.0%) than in medium populated or rural areas (4.9%
and 3.7%, respectively). On
the contrary, the classical at-risk-of poverty rate is lower in
cities (19.2%) than in towns
(22.1%) and rural areas (25.9%). The risk of poverty based on
Yidc increases the poverty
rate in cities (+ 1.9) and towns and suburbs (+ 1.3) but
decreases the poverty rate in rural
areas (− 1.0). The sensitivity is increased in all cases and, in
this regard, it is worth noting
that in rural areas the monetary poverty rate based on Yidc
(24.9%) is lower than the classi-
cal one (25.9%) but the sensitivity is increased + 7.0 percentage
points (75.4%).
4 Conclusions
This study investigates the extension of the classical monetary
poverty measurement to a
two-dimensional approach, trying to refine the current link with
material deprivation that is
a direct poverty measurement. It broadens the classical one-
dimensional disposable income
model and makes it applicable to other monetary variables, for
example monetary con-
sumption, via the distribution function due to the fact that the
poverty risk rate coincides
with the value of this distribution function evaluated on the
poverty threshold. Building
from here, a two-dimensional poverty risk rate (income-
consumption) based on the cen-
troid determined by the respective one-dimensional thresholds
is defined. This rate is seen

Fig. 3 At-risk of poverty rates (x-axis) and severe material
deprivation (y-axis) by region, year 2017.
Source: Prepared by the authors based on data presented in
Table 7
Table 8 At risk of poverty rates
and sensitivity (%) by degree of
urbanisation, year 2017. Source:
Prepared by the authors based on
LCS anonymised microdata
Degree of urbanisation SMD rate Yd,60 Yidc,60
Arop.R Se. Arop.R Se.
1. Cities 6.0 19.2 63.3 21.1 68.0
2. Towns and suburbs 4.9 22.1 61.4 23.4 65.0
3. Rural areas 3.7 25.9 68.4 24.9 75.4
Total 5.1 21.6 63.8 22.6 68.7
1 3
to show a stronger association in terms of correlations with
material poverty than the two
one-dimensional variables it is based on.
In this context the join distribution of monetary income and
consumption at micro data
level is explored, paying special attention to the left side of the
distribution based on the
two poverty thresholds that determine the centroid
(yline,60,cline,60). The analysis of the

two-dimensional poverty risk rate (income-consumption) makes
it possible to determine
two typologies. On the one hand, of those individuals whose
consumption is more clearly
linked to their income, both those who are located below both
poverty thresholds (area I),
and those whose levels of income and expenditure are above the
two poverty lines should
be considered (area IV). On the other hand, of those individuals
with a less clear asso-
ciation between income and consumption that, additionally,
allow us to consider another
two different situations: the first consists of individuals who
have a low level of equivalent
income but whose levels of monetary expenditure are above the
consumption poverty line
(that is, area II), which could conceal situations of consumption
financed by means of pre-
viously accumulated wealth, debts, family assistance or even
informal economy activities,
which would mean an infra declaration of income and that such
individuals could not be
really in a situation of material deprivation; the second would
consist of individuals with
a low level of monetary expenditure but whose income levels
are medium–high since, in
this case, they are to be found above the income poverty line
(that is, area III), who are
normally individuals facing debt service, usually a mortgage
linked to home purchase. The
interpretation of the latter situation provides an additional
reason for the incorporation,
with monetary variables, of the expenses and/or income related
with net wealth as carried
out in this study.

At this point we explore whether a linear combination of
monetary income and con-
sumption may offer a refinement of the classical approach to the
monetary poverty. Since
expenditure on housing is determinant in households in the first
income quintile, and with
the restriction of using empirical information based on official
sources of statistics, the
solution applied is to consider in the EU-SILC area the
equivalised income characterized
by expenditure on housing, with and without imputed rents.
The area under the curve obtained for Yd, Yid, Ydc and Yidc in
2017 are 0.82, 0.84, 0.83
and 0.84, respectively. These results are between 0.8 and 0.9
and can be considered as
excellent (Mandrekar 2010) particularly in the cases of Yid and
Yidc since they offer a
slight improvement of the AUC compared to the classical Yd.
Concerning the temporal
dimension, which covers the decade 2008-2017, the associations
measured via the cor-
relation coefficients between severe material deprivation and
the risk of poverty rates for
this period offer better coefficients being obtained with the
characterized income adding
imputed rents, Yidc. The internal test at micro level,
meanwhile, also throws up the result
that, once again, Yidc has a greater sensitivity than the classical
Yd (+ 10.8 percentage points
in 2011) while the specificity and accuracy are always rather
similar (around 80%). As far
as the spatial dimension is concerned, the internal test is carried
out via the analysis of the
sensitivity of the indicator to severe material deprivation; when
using the characterized

income adding imputed rents Yidc this value increases in most
of the autonomous commu-
nities reaching the maximum intersection of 100% in some
regions. On the other hand, the
external test is carried out with the results obtained in the
seventeen Spanish autonomous
communities at the NUTS level and leads to the conclusion that
the characterized income
Yidc also increases the coefficient of determination and
correlation with material depriva-
tion. The results achieved are also more consistent when an
analysis by degree of urbanisa-
tion is carried out, particularly in the cases of rural areas and
cities. We can therefore con-
clude in this case that, from an empirical point of view, the
poverty risk rate obtained using
1 3
the equivalent characterized income adding imputed rents Yidc
is an indicator that succeeds
in refining the good results of the classical poverty risk
indicator, in both its temporal and
spatial dimensions.
Notwithstanding the good results achieved there are also some
opportunities and limita-
tions to be considered. The case presented in this study focuses
the analysis in a country
of the European Union and, at this stage, the conclusions should
be limited to a context
of complementary rather than substitutability of the classical

Yd, which is an international
standard. In addition, the applied approach is exclusively
focused on monetary poverty var-
iables in order to better measure the effect of the refinement,
but it could also be extended
by adapting the percentage p introduced in Eq. (1) instead of
considered it as a constant
parameter defined by convention, 60% in the European Union,
or by introducing other mul-
tidimensional indicators to measure the poor, not only in
developed countries (García-Pérez
et al. 2016) but also by the different regions (Jurado and Pérez-
Mayo 2012) and, especially,
if regional purchase parities were applied to the equivalence
scales. Influence of risk fac-
tors of income poverty and severe material deprivation (Verbunt
and Guio 2019) is another
element that could be taken into consideration for widening the
analysis. Finally, to be able
to conclude a joint monetary income and consumption analysis
it would be very interesting
to have empirical data containing the two-dimensional patterns
of households/individuals
together with a direct measure of poverty, particularly severe
material deprivation.
This study allows us to continue a line of research that seeks to
improve the measure-
ment of monetary poverty from a multidimensional perspective
(Santos and Villatoro
2018), on this occasion by integrating the two visions of
monetary poverty based on
income and consumption according to UNECE, and also laying
the foundations of a poten-
tial conceptual convergence between residual income and
characterized income indicators,

with the consequent improvement of them.
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Pfaff et al. BMC Public Health (2021) 21:2253
https://doi.org/10.1186/s12889-021-12256-9
R E S E A R C H A R T I C L E
The little things are big: evaluation
of a compassionate community approach
for promoting the health of vulnerable persons
Kathryn Pfaff1* , Heather Krohn1, Jamie Crawley1, Michelle
Howard2, Pooya Moradian Zadeh3, Felicia Varacalli1,
Padma Ravi1 and Deborah Sattler4
Abstract
Background: Vulnerable persons are individuals whose life

situations create or exacerbate vulnerabilities, such as low
income, housing insecurity and social isolation. Vulnerable
people often receive a patchwork of health and social care
services that does not appropriately address their needs. The
cost of health and social care services escalate when
these individuals live without appropriate supports.
Compassionate Communities apply a population health theory
of
practice wherein citizens are mobilized along with health and
social care supports to holistically address the needs of
persons experiencing vulnerabilities.
Aim: The purpose of this study was to evaluate the
implementation of a compassionate community intervention for
vulnerable persons in Windsor Ontario, Canada.
Methods: This applied qualitative study was informed by the
Consolidated Framework for Implementation Research.
We collected and analyzed focus group and interview data from
16 program stakeholders: eight program clients,
three program coordinators, two case managers from the
regional health authority, one administrator from a part-
nering community program, and two nursing student volunteers
in March through June 2018. An iterative analytic
process was applied to understand what aspects of the program
work where and why.
Results: The findings suggest that the program acts as a safety
net that supports people who are falling through
the cracks of the formal care system. The ‘little things’ often
had the biggest impact on client well-being and care
delivery. The big and little things were achieved through three
key processes: taking time, advocating for services and
resources, and empowering clients to set personal health goals
and make authentic community connections.

Conclusion: Compassionate Communities can address the
holistic, personalized, and client-centred needs of people
experiencing homelessness and/or low income and social
isolation. Volunteers are often untapped health and social
care capital that can be mobilized to promote the health of
vulnerable persons. Student volunteers may benefit from
experiencing and responding to the needs of a community’s
most vulnerable members.
Keywords: Vulnerable populations, Homeless persons,
Community participation, Program evaluation,
Compassionate communities, Health services research,
Implementation science, Qualitative research
© The Author(s) 2021. Open Access This article is licensed
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line to the data.
Background
Vulnerable populations experience significa nt barriers
accessing social, economic, political, and environmen-
tal resources [1, 2]. The result is poorer health. Without
resources, these persons become unable to protect or
Open Access
*Correspondence: [email protected]
1 Faculty of Nursing, University of Windsor, Windsor, Canada
Full list of author information is available at the end of the
article
Page 2 of 10Pfaff et al. BMC Public Health (2021)
21:2253
care for themselves, either permanently or temporarily,
often due to physical, mental, emotional or other causes
[3, 4]. While there is debate surrounding the term ‘vul-
nerability’, its indicators include homelessness or housing
insecurity, low-income, physical or mental frailty, social
isolation, and having a physical or mental disability [3, 5].
For the purpose of this study, we use these criteria as our
definition of vulnerability.
Low income is the most significant predictor of experi -
encing vulnerability [3, 5]. In Canada, almost one-tenth
of the population experiences low income [5]. Nearly one
in five Canadians who rent housing spend more than 50%
of their income solely on rent [6], putting them at risk
of homelessness [7]. One quarter of a million Canadians

experience homelessness, and every night, 35,000 peo-
ple sleep in parks and on the streets [6]. These statistics
do not include the hidden homeless. The hidden home-
less lack permanent housing and frequently sleep in their
cars or ‘couch-surf ’; the latter involves relying on family,
friends for providing sleeping accommodations [8, 9].
Accordingly, 2.3 million Canadians report experienc-
ing hidden homelessness at some point in their lives [9].
Regardless of homelessness type, these people experience
significant challenges in finding a job, living a healthy
lifestyle, and maintaining relationships with others [10].
People who experience homelessness are at greater risk
for acute and chronic illnesses [11] and the chance of liv-
ing until the age of 75 is approximately 32% in males and
60% in females [12]. Sadly, they may only receive a patch-
work of health and social care services that are often not
well coordinated.
Eliminating health care and social service gaps and
reducing barriers to accessing care is challenging at the
individual, community, and population health levels. In
Canada, funding is insufficient to address the housing
needs of low-income citizens, and there are inadequate
numbers and availability of shelter beds [13]. People
experiencing low income and homelessness often feel
stigma and therefore, lack trust in providers when access-
ing care [14]. People who experience indicators of vul -
nerability may not view these indicators as problematic
[3] making identification, engagement, and intervention
difficult.
The compassionate community movement
Compassionate Communities (CCs) are spreading world-
wide but are relatively new in Canada. The CC move-
ment is a population-based theory of practice that calls
on society to intentionally contribute to caring for its

citizens [15], especially those experiencing indicators
of vulnerability. In this model, citizens are purpose-
fully mobilized as volunteers with health and social care
institutions to help people in need identify their own
person-centred goals for living well. People are then con-
nected with community resources and empowered to act
on their goals and needs. With collective engagement,
a CC becomes an interplay of caring actions with and
among a community, its citizens, and health/social care
organizations [15].
In Canada, the CC movement is led by a collective of
palliative care stakeholder organizations [16–18], but the
approach is adaptable for people of wide-ranging health
needs and vulnerabilities. The CC theory of practice can
be implemented to best suit a community’s priorities,
needs, and resources. When strategically put into prac-
tice, CCs can improve the quality of life for persons living
with precarious health, social and environmental circum-
stances [19].
The Windsor‑ Essex compassion care community
The Windsor-Essex Compassion Care Community
(WECCC) is a collective of volunteers and 65 health/
social care organizations that partner in identifying and
reducing the unmet needs of persons living with complex
health and social issues [19]. Target populations include
seniors, the frail elderly, people with chronic disease and
disabilities, and people living in social isolation. WECCC
staff and volunteers assist clients to identify their own
personal needs, goals, and preferred interventions.
The Vulnerable Persons (VP) Program, a sub-project of
the WECCC, was born out of a need to provide focused
support for people living with low income and housing

insecurity in Windsor-Essex, Ontario Canada. In col-
laboration with the regional health authority, Family Ser-
vices Windsor-Essex, the Hospice of Windsor and Essex
County, the primary care sector and others, VP program
staff and volunteers have worked with over 400 individu-
als to develop goals that address their unmet health and
social needs. Clients are never discharged, and service
level is determined by client need. Programming var-
ies from face-to-face intervention with fully integrated
health and social care supports, to scheduled check-
in calls by staff and volunteers for assessing client goal
achievement and quality of life.
Currently, little is known about the experiences of CC
stakeholders and how to successfully implement CCs
among vulnerable persons. This information is needed
to improve and spread this program and to inform oth-
ers who are implementing similar initiatives. The purpose
of this exploratory study was to evaluate the implemen-
tation of a compassionate community intervention for
vulnerable persons in Windsor Ontario, Canada. In par-
ticular, we sought to describe and interpret stakeholder
experiences about the program’s characteristics, its pro-
cesses, and potential impacts and opportunities.
21:2253
Methods
We employed an applied qualitative approach [20] to
describe and interpret stakeholder perspectives about the
VP program. This approach enabled us to critically exam-
ine the data to develop a rich understanding of the stake-
holder experiences, the program’s processes, its impacts,

and areas for improvement. WECCC’s research and eval -
uation program is guided by constructs of the Consoli-
dated Framework for Implementation Research (CFIR)
[21, 22]. In this evaluation we focused on several con-
structs within its domains - characteristics of individuals,
intervention characteristics, outer setting, and program
processes [21]. We deemed them to be the core domains
on which to focus for understanding the ‘what’ and ‘how’
of the VP program implementation.
Sample and recruitment
We used convenience sampling to identify individuals
who met the following criteria: (1) being either a pro-
gram client or a stakeholder who is actively engaged in
program delivery, (2) over the age of 18 and (3) English
speaking. Participants were recruited by WECCC office
staff using a structured script over a four-month period
of time between March and June 2018. The final sample
included 16 program stakeholders made up of three VP
coordinators, two community case managers from the
regional health authority, one administrator of a key part-
ner community program, two nursing student volunteers
who had completed a community clinical experience
with the VP program, and eight VP clients. Among the
VP clients, one person was experiencing homelessness
at the time of data collection. The remaining clients were
previously homeless but living in temporary and/or pre-
carious living situations.
Data collection
We conducted one focus group with five VP clients and
individual telephone interviews with three clients who
were unable to attend the focus group. A focus group was
purposefully selected as we sought to gather and validate
collective client perspectives about the program. The
focus group took place at the Hospice of Windsor and

Essex County and transportation to the Hospice was pro-
vided for VP clients. It was facilitated by JC. Field notes
were documented by HK and observations noted by KP.
Individual telephone interviews were also completed with
the VP care coordinators, the community care case man-
agers, the partner program administrator, and the stu-
dent volunteers. These interviews were completed by JC,
HK, and KP. The interview guide was developed for this
study with questions and prompts informed by the CFIR
Interview Guide tool [21]. Refer to Supplementary 1. The
same interview schedule was used for all stakeholders. .
All focus group and interview data were digitally audio-
recorded and transcribed verbatim by a trained tran-
scriptionist and research assistants. Two VP coordinators
and three participants agreed to engage in member check
interviews in which we shared the emerging themes and
invited them to confirm, disconfirm, and offer further
explanations. There was agreement from participants
regarding the emerging findings. We confirmed data
redundancy for the overall themes and therefore ceased
data collection.
Analysis
We applied Sally Thorne’s pragmatic approach to
selecting data analysis procedures [20]. Three nursing
researchers (KP, HK and JC) and a research assistant
(FV) iteratively reviewed the transcripts individually.
We met as a team to discuss early insights and potential
codes. A codebook was established to support early cod-
ing and researcher consistency with coding. During open
coding, new codes were created and documented on the
transcripts by each member of the team. We simultane-
ously extracted meaningful/powerful quotes to a shared
word document. During weekly team meetings, codes
were reviewed, revised and some were abandoned as

they were deemed to not reflect the data. Throughout
the process, we applied a constant comparative approach
[23] to the data comparison, and re-organization of the
data into categories that were later collapsed into emerg-
ing themes. During this time, we considered a range of
possibilities, took care to avoid premature closure [20]
and documented our decisions. We met weekly in the
last 3 weeks of analysis to agree upon the overall theme
and its sub-categories. We collated and shared our indi-
vidual memos and analytic insights in face-to-face dis-
cussions, and then mapped these notes to our themes as
a method for validating our interpretations. Decisions
were reached by consensus and the findings were unani-
mously approved by the team.
Ethical considerations
Ethics clearance was granted by the University of Wind-
sor Research Ethics Board (REB# 16–047). Consent was
gathered and documented individually for all participants
in both the focus groups and the interviews. All partici -
pants were invited to create and share their preferred
pseudonyms and to ask any questions of the researchers
before beginning the focus groups and interviews. Focus
group participants were reminded that confidentiality
could not be assured due to the group nature of data col-
lection, but participants agreed to not share information
provided by others. Client participants were assured that
their decision to participate (or not participate) would
have no impact on their program services.
21:2253
Results

The findings are organized and presented in the following
sections: (1) participant characteristics, (2) intervention
characteristics, (3) program processes, and (4) impacts
and opportunities for improvement.
Participant characteristics
Vulnerable clients were described (by self and providers)
as “invisible” within the system and being socially iso-
lated. They were also characterized by providers as hav-
ing “brittle support systems”, being disconnected from
family, and having “no one looking out for them.” Cli-
ents and providers described complex health issues that
include, but were not limited to developmental disabili -
ties, anxiety, depression, renal failure, immobility, and
pain. Life challenges that prompted referral to the pro-
gram included homelessness, financial insecurity, elder
abuse, bereavement, and caregiver burden. As stated
by one care coordinator: “Life has kind of dealt them a
crappy hand. A lot of times it’s about the social determi-
nants of health and some people just aren’t as privileged
as others … and there’s just not the supports in place, or
there are supports but they’re not readily acceptable to
people and it prevents them from really getting the help
that they need …” (VP Coordinator 2).
Intervention Characteristics - The Little Things are Big.
The analysis revealed one overarching meta-theme that
describes the characteristics of the intervention, ‘the little
things are big’. Clients and providers frequently referred
to the program’s ability to address ‘the little things’ that
often go unnoticed at the systems level, but that have a
big impact on client health and quality of life: “We have
fairly large caseloads and we don’t have like the days to
spend working on the smaller tasks that are big for our
patient. Like we put in the care plans, we put in the ser-

vices for them but...it was the little things, like she [the cli -
ent] wasn’t able to wash her hair and her daughter was
burning out and didn’t have contact with anyone in the
community.” (Community case manager).
The ‘little things’ commonly involved assisting with per-
sonal and practical needs (personal care, groceries, meal
preparation, finances, home maintenance) that kept cli-
ents healthy, safe, and in some cases prevented them from
being evicted and/or being able to remain in their homes.
“I have a gentleman that I’m working with, he’s got ALS
[Amyotrophic Lateral Sclerosis] and he needs somebody to
go to his house and just help him get his lunch from his
stove to his table, that’s it. I mean it seems like such a sim-
ple thing, but he had a great deal with difficultly doing
that” (VP care coordinator 1). Jane, a VP client, shared
the following: “He helped me with my portable air con-
ditioner, setting it up and so we got it running...I have a
medical alert button and he put it all together...and made
a phone call that took like an hour with these people, but
he saved me $300 …”.
Social support was perceived as a little but significant
thing for clients, staff and students “A lot of people think
you have to constantly be doing physical things for people
or sending referrals, but a lot of people just want someone
to talk to … especially vulnerable people who don’t often
get the opportunity to just sit and chat with somebody. It
is very beneficial, and I love seeing people’s lives change,
specifically for the better, just through the support that
we’re able to give them” (VP coordinator 2).
Some clients received friendly visits from WECCC
volunteers and/or were connected with WECCC’s com-
munity partner programs. All participants described the

importance of therapeutic communication and listening
skills as being the core component of the program inter-
vention. Molly stated: “That was a comfort to me to know
that there were individuals concerned with little, old me
in the sense that … we all come from different parts of a
community and they’ve included everybody and that’s a
very emotional thing for me to have support from people
that I don’t even know.”
Clients receive ongoing, free support and this was per-
ceived as a “big thing” for clients and the program.
“Obviously there’s no cost so that’s a big thing, but we
never tell them they’re discharged from our program
so we can see them once a week until they feel sup-
ported which I think is a huge relief for them because
they don’t want to tell us their whole story, be done
with us in four weeks and then move on to their next
worker. So that’s a big thing for us. Even when …
we’re not seeing them on a weekly basis, we continue
check-in calls whether that be monthly, six months,
one year so it’s a huge relief for them … to know they
always have our support (VP coordinator 3).
Program processes
The little and big things are addressed through three key
processes: (1) taking time, (2) advocacy, and (3) empow -
erment. Each process is reported in the following text.
Taking time
Taking time was a key process that enabled the sub-pro-
cess of advocacy and empowerment: “… when we go to
a home, we’re having a conversation with the client at a
pace that’s appropriate to them with intentions of build-
ing a report with client and in doing so, they begin opening
up about things that they want to work on, difficulties that

they’re having that they often have not shared with other
people...” (VP coordinator 1). The importance of this pro-
cess was echoed by another care coordinator: “Most other
professionals that go out to people’s homes, they are so
21:2253
focused … that it’s a pretty quick conversation. Whereas
our conversations are much more open ended … ‘so what
is it that you think you could do? What would you need to
improve [your] quality of life?’ This is a very big question
and is not trying to fit their answer into some predeter-
mined kind of things that you can offer. So, it takes time”
(VP coordinator, 2).
Time spent listening and communicating therapeuti-
cally was highly valued, whether it occurred face-to-face
or by telephone - “I appreciate how they don’t just [say]
o.k. here is what we talked about last visit and drop a
bunch of papers in front of you and you know it’s all curt
like it is with a lot of offices you know. They take the time
to discuss with you between your options which ones are
best for you …” (Hunch).
Receiving a monthly check-in call was the most fre-
quently valued intervention reported by clients and pro-
viders. In some cases, the call filled a gap when other
services had run out and offered a sense of security and
social connectedness: The client asked me when I talked
to him last, ‘Would you be able to call and just check up
to make sure I’m doing okay? Can you please call me in
a month just to check in?’... So, I’ll call again in another
month (VP coordinator 2).

Advocacy
The process of advocacy encompassed activities such as
researching programs and services, contacting providers
and community organizations, and explaining the client’s
complex health and living situation. Advocacy work was
successful in securing vital care and services, such as free
and/or affordable transportation for clients, funding for
medical equipment, prescription medications, assistance
with activities of daily living, and temporary housing.
A community case manager from the regional health
authority described the following example of advocacy
work:
“I have this mid 70s lady who falls into the category
of having a brittle support system, had a fire in the
summer in her condo, she’s on hemodialysis. She …
was missing dialysis a lot, was going to the ER with
shortness of breath … A constant ride to dialysis was
the reason she was missing it plus she was suffering
some depression … It took a lot of coordination, but
we were able to get her rides. I was able to get her
providers to start early, to get her ready for dialysis,
get her on and off transport … WECCC dug deeper
and was able to connect with the social worker and
found funding to get this ride and now her dialysis
times have been changed... The patient is now going
to dialysis.”
Greg shared an example of advocacy when facing home-
lessness after being discharged following a recent hospi -
talization: “I’ve been [living] with a broken back over 10
years ago when I went backwards down the basement
stairs … I ended up with a fractured skull and a cerebral
hemorrhage … He [VP care coordinator] helped me find

a place and he booked me in a [rest home] for about nine
months … and did some work on getting me an electric
scooter …. [my] mobility is not getting better...”
Empowerment
Writing personal health goals was identified as the key
process for client empowerment by six clients and all of
the other stakeholders: “I think the most important part
of it is the establishing of SMART [Specific, Measurable,
Attainable, Relevant, Time-Bound] goals. Those provide
direction and they also help actually motivate the clients
to achieve the goal that they have identified.” (VP coordi -
nator 1).
Some participants were affirmed by the power of goal
setting for clients living in precarious life circumstances.
Shawn explained: “You would like never put that two and
two together yourself but to have somebody say to you
‘Yes you know this is something that you can do.’ That just
makes you feel productive as a person, definitely.” The pro-
gram administrator from a partner community program
shared the following: “One of our clients who is palliative
… there’s a persistent level of depression … but you know
she was still able to make some goals. She was still able
identify that ‘I would want to do this, this and this’ before
it all ends for her.”
The nursing students validated the value and power of
goal setting for empowerment. Brianna explained: “You’re
asking them ‘what can you do to improve your quality of
life?’...and it helps people realize, ‘Oh I can change this. I
don’t want this to be my life the way it is’ and we help with
figuring it out … A lot of the time too we would help make
goals for the patients and say, ‘o.k. we’ll do this to help
you get to here’ and by the time we called the next week...
they’ve done it on their own.”

Making social connections was one of the most iden-
tified goals reported by clients and staff. “Getting out
more is the number one reason people are referred to us,
it’s just people are so isolated and so getting out more is
one of the biggest goals” (VP Coordinator 2). Clients were
empowered to improve their social connections and their
personal well-being through intentional connections
to community activities, such as card groups and yoga.
“We’re empowering them to create one linkage that leads
to the next in the community so just getting them involved
in other programs so they have some kind of care circle in
their life” (VP Coordinator 3). Transportation provided by
the local hospice enabled attendance at some programs.
21:2253
“You know I get to meet people and get out of here...with
the rheumatoid I wake up with pain every day...my goal is
to get back swimming … See if that benefits it [the pain].
There’s other things Hospice offers, other programs, ‘Living
with Chronic Pain’ is one of them” (Greg).
A few clients were empowered to use their talents to
give back to the community. “We had one client who
was good at knitting or crocheting so we suggested that
maybe she find a program where she could knit, knit hats
for babies... … we did have a few clients who were heav-
ily involved in advocacy for low income people as well as
homeless people … (nursing student volunteer). One par-
ticipant described how she made woven mats from plas-
tic milk bags for people living on the streets, and another
participant set up a Facebook group to promote social

connection and advocacy for the homeless and those at
risk for homelessness.
Impacts and opportunities for improvement
The qualitative data suggest positive health impacts for
clients and benefits for the community and the health
care system. In most situations, the program serves as a
safety net that supports people who are falling through
the cracks of the formal care system.
“One of our clients, who had a stroke, she lost func-
tion in her right arm and her right leg … she was
given a manual wheel chair and for two years she
lived in an environment in which she was literally
going in circles right because she didn’t have use in
one of her arms in order to keep this wheel chair
straight and she lived like that for two years! To me
that sounded like an absolute system failure but one
that really could have been avoided had she called
the system, called the LHIN, called the doctor, any
of these kinds of people who would’ve been able to
intervene and should have intervened but she didn’t
… if our clients are receiving monthly check-in calls,
something like that will not happen.” (VP Coordina-
tor 1)
All of the clients reported benefits from increased social
interaction and a connection with their community. The
participants described multiple examples of how the
program is directly reducing use of emergency services,
preventing homelessness, improving client safety, and in
a few cases, averting attempted and completed suicides.
“One member tried to kill himself … When he was
released, he was sent to our program through the
social worker. He had no other supports just himself

and he lives with his brother, so we set him up with
the crisis number and made a goal for him and his
brother to be a support system for one another. They
get out walking at least once a week and they hold
each other accountable … He also wanted to work
part time … so I gave him a number to the unem-
ployment centre in his area, and he reached out
to them himself … created his own resume, and he
actually landed himself a job” (VP coordinator 3).
Although long-term community investments are needed,
short-term support for securing safe housing and pre-
venting homelessness was reported as a positive impact
of the program: One client with hoarding behaviours
described how the program enabled her to avoid evic-
tion by negotiating a plan to reduce the clutter: “I had the
fire marshal come in here … my house was ransacked and
then I just let it go because I suffer from depression … and
alcoholism. She’s (the landlord) given me like a week to get
one room done and a week to get another room done and
he’s [the volunteer] helping me out … He’s helped me out,
period! (Jane).
All eight clients reported support for managing chronic
health issues such as pain, anxiety, depression, renal
failure, and diabetes. Coordinators were able to assist
clients to access dialysis appointments, prescription
medications, and primary care in cases where clients
had no family physician. In some situations, the coordi-
nator attended primary care visits to add context to the
situation. A nursing student volunteer discussed success
with helping a client navigate management of her chronic
pain:
“I had a patient who was in chronic pain and she

had no, she didn’t have a family doctor, she didn’t
have any management of her pain at all. She had
tried non-pharmacological things and it wasn’t
working, so she was sleeping until 2:00 pm every day
and then going to bed early cause … she couldn’t
function … Her main thing was figuring out that
pain … she was so socially isolated … because she
couldn’t handle it … We got her a family doctor.
We had VON [Victorian Order of Nurses] connect
with her to help with pain management and we also
signed her up with hospice … so that when she had
that pain managed then we can work around the
social isolation which was getting her involved with
the wellness program in the community.”
Through this evaluation, we learned from care coordina-
tors and volunteers that training programs should include
specific content and tools for responding to the needs of
individuals experiencing complex mental health con-
cerns. Sustainability opportunities include technology,
funding, and volunteers. Many clients do not have inter-
net, electronic devices, and/or are not tech savvy. Per-
manent funding for program coordination and volunteer
21:2253
training will be essential for long-term sustainability. As
stated by one coordinator: “I think the main resource that
we require more than anything is volunteers … I think that
is the key to it all. For myself, I am struggling to keep up
just because of the kind of manpower issue. And the main
reason for that … you don’t have a volunteer base big
enough in order to be able to have that time.”

Discussion
This study is important as it adds to the growing inter-
national evidence about the positive impacts of CCs
on individual and community health. The majority of
the evidence is published in the palliative and end-of-
life care literature [24]. This is the first study to evalu-
ate a CC approach within a targeted vulnerable care
sector. The results of this evaluation demonstrate that
health and social care sectors can be mobilized by a CC
approach to holistically address the big and little needs
of society’s most vulnerable and invisible persons. The
qualitative findings suggest that the ‘little things’ often
had the biggest impact on client well-being and on care
management. The ‘big and little things’ characterize the
VP intervention, and they were addressed through the
processes of taking time, advocacy and empowerment.
In this study, these processes appear to address vulner -
abilities, such as housing security, physical and mental
disabilities, and social isolation. They also meaningfully
address the holistic concerns that were most pressing and
important to program clients, with social isolation being
a significant concern. Recruiting and retaining volunteers
is the most key opportunity for improvement and sus-
tainability of the program.
The Canadian healthcare system and those of other
countries remain entrenched in approaches that are
largely siloed and not coordinated to meaningfully
address the things that are most valued by people and
that contribute to their quality of life [25, 26]. It is over -
time for policy experts and governments to prioritize
an integrated system of health and social care that takes
action on the ‘little things’ that often have a big impact
on health. Food, transportation, safety, and social con-
nectedness were described by participants as “little”. Yet,

they are basic human needs that are essential for health
and quality of life, and they are frequently not accessible
to those facing vulnerabilities [27].
In Canada, there are very few community-based pro-
grams that provide holistic, and continuous life-long sup-
port for people who experience homelessness, housing
insecurity and low income, and some of these programs
are often criticized for reinforcing obstacles to engage-
ment [28]. International community-based programs
face similar criticisms. Many have criteria that are based
on age or gender [29, 30], chronic and advanced disease
[31–33], and/or on addiction or mental health issues [31,
33–35]. Others focus on interactive educational work-
shops and are time-limited or transitional [35]. Program
benefits are often not maintained long-term [29, 32, 34]
with clients reverting back to their original behaviours or
circumstances after support is withdrawn. The VP pro-
gram addresses these gaps and challenges by purposefully
organizing communities to act on the big and little things
as an issue of public health through its key processes.
Enacting the key processes
Taking time, advocacy, and client empowerment are pro-
cesses that can be readily enacted at the community level,
but we argue that the processes must be systematically
integrated into the system to be effective and sustainable.
This systems-level change will require reconsideration of
funding and service delivery, not just a shuffling of deck
chairs [36] or one-off programs. CCs address these bar-
riers by adopting a public health approach that seeks to
truly understand what is most important to people where
they live and by engaging people and communities to
act on addressing the needs of people experiencing vari -
ous health and social care issues [15, 37]. Effective, resil -

ient, and sustainable health and social care systems can
be achieved when vulnerable persons are empowered as
active advisors and partners in re-shaping change [2].
The process theme of empowerment resonates with this
notion in that people with vulnerabilities were empow -
ered to act on their own goals, and support others in
their own community.
Advocacy is an important public health tool for
addressing the social determinants of health and an
important process for addressing care gaps and ineq-
uities within the system [38]. Unfortunately, advocacy
takes time, and time is a scarce resource for many health
care providers [39]. Current healthcare systems reward
efficiency and larger volumes of clients [40], potentially
discouraging providers from taking the needed time to
address a person’s holistic care needs. Mounting schol-
arly CC evidence is showing that caring interactions
among persons, families, neighbours, and healthcare pro-
viders enable participatory care [37, 41], improve overall
wellbeing and reduce mortality [37, 41, 42], and ease the
burden on the care system [41]. This study supports and
adds to this body of evidence.
In a CC model, volunteers are often untapped health
and social care capital who have time to give back to
their communities and can be mobilized in action [43].
The findings of this study again support the notion
that volunteers can be equipped with the skills needed
to advocate for and empower people who are experi-
encing vulnerabilities. At the time of this study, the VP
program had 50 trained volunteers, including students

21:2253
from nursing, social work, and gerontology. Twenty
additional volunteers were trained during the first 6
months of the COVID-19 pandemic to provide vir-
tual check-in visits with VP clients. Because negative
or inaccurate perceptions of homelessness, poverty
and other types of vulnerabilities can result in deficit
versus strength-based practices that further stigmatize
clients [44], formal training is essential. VP volunteers
are overseen by the VP care coordinators, and they
participate in mandatory and specific volunteer train-
ing in areas such as diversity, communication in com-
plex client scenarios, goal setting, the importance of
socially connectedness, and self-care.
With regard to both empowerment and advocacy,
this program provides a window through which nurs-
ing student volunteers viewed the realities of those
experiencing vulnerabilities, and an opportunity to
integrate service learning into CCs. Nursing students
reported similar benefits to those described by Knecht
and Fischer - shattering their own stereotypes, recip-
rocal learning through relational practice, and devel -
oping skills in community advocacy [45]. An added
benefit of student volunteers is that they are truly able
to take the time to comprehensively assess and address
the breadth and depth of client needs – time that is
not typically available in other clinical learning set-
tings [45].
Addressing social isolation
The need for improved social connectedness among VP
clients is an important secondary finding of this study.
Social isolation has been deemed a public health pan-
demic [46], a predictor of early mortality [40] and a

common experience of all clients in this study. Social
exclusion negatively affects the subjective wellbeing of
those who experience homelessness, and interventions
that engage people in building social connections will
improve their quality of life [37]. Overcoming social
isolation by expanding social networks is a key focus of
the VP program, and its approach is informed by good
evidence [40, 47, 48]. The VP program engages high
risk individuals as active participants rather than pas-
sive recipients and empowers their participation in the
planning and implementation of social engagement.
Support is flexible and adaptable to the needs and goals
of the participants, and it is rooted in both the com-
munity and the person’s own social network. An unin-
tended and serendipitous benefit of the focus group
was that participants identified creative opportuni-
ties for supporting the local homeless community and
agreed to share contact information as a way of staying
connected after the study concluded.
Embracing discomfort
As stated by Karen Armstrong, founder of the global
Charter for Compassion.
movement, “A compassionate city is an uncomfortable
city! A city that is uncomfortable when anyone is home-
less and hungry …” [49]. We add that a compassionate
community should also be uncomfortable when any citi-
zen is socially isolated or lonely. As the socioeconomic
inequalities in health and other indicators of vulnerability
continue to widen in Canada [50] and around the world
[2, 25], it is time for every community to be very uncom-
fortable – uncomfortable to the point that every citizen is
treated “as we would wish to be treated” [49] and empow -
ered to take the time to take action on inequities.

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