This document summarizes research conducted by MKT 8200 on mobile market research startup 1Q. It includes the following key points:
1) 1Q is a mobile market research startup that pays users small amounts to answer survey questions and shares location data with partner companies. The research aims to help 1Q increase active user engagement.
2) Surveys of 1Q users found that intrinsic motivations like having their voice heard are more influential than monetary compensation in driving user engagement. Companies can effectively use surveys for engagement without offering rewards.
3) Analysis of factors influencing users' likelihood to recommend 1Q found that early adopters, those likely to share responses, and satisfied users were most likely to recommend. You
The document discusses using regression for association testing and prediction. It provides an example analyzing factors associated with memory scores in HIV patients. A linear regression found larger household size was associated with higher memory scores when controlling for age and clinic. This provides evidence that household size may contribute to memory loss in HIV patients. The document also discusses using regression for prediction, comparing models, and measures for predictive accuracy like AUC.
Presentation at the European Central Bank, Nov 6, 2013
Panel surveys are used to measure change over time, but previous research has shown that simply asking the same questions of the same respondents in repeated interviews leads to overreporting of change. With proactive dependent interviewing, responses from the previous interview are preloaded into the questionnaire, and respondents are reminded of this information before being asked about their current situation. Existing research has shown that dependent interviewing techniques can reduce spurious change in wave-to-wave reports and thus improve the quality of estimates from longitudinal data. However, the literature provides little guidance on how such questions should be worded. After reminding a respondent of her report in the last wave (“Last time we interviewed you, you said that you were not employed”), we might ask: “Is that still the case?”; “Has that changed?”; “Is that still the case or has that changed?”; or we might ask the original question again: “What is your current labour market activity?”. In this study we present experimental evidence from a longitudinal telephone survey in Germany (n=1500) in which we experimentally manipulated the wording of the dependent questions and contrasted them with independent questions. We report differences in the responses collected by the different question types. Due to the concern that respondents may falsely confirm previous information as still applying, leading to underreporting of change in dependent interviewing, we also test hypotheses about how respondents answer such questions. In these tests, we focus on the roles played by personality, deliberate misreporting to shorten the interview, least effort strategies and cognitive ability in the response process to dependent questions. The paper provides evidence-based guidance on questionnaire design for panel surveys.
joint work in Annette Jaeckle, University of Essex
The document summarizes a student's community health internship project examining car seat safety compliance using the Theory of Planned Behavior framework. Key findings include:
- Rates of rear-facing car seat compliance were high at 87% based on a survey of 110 parents/caregivers.
- Factors like education level, income, and doctor recommendations influenced compliance levels.
- The Theory of Planned Behavior was highly predictive of intentions and behavior, while the Health Belief Model showed no significance with the measures used.
- Further outreach is needed targeting non-compliers, low education/income groups, and emphasizing rear-facing to age 2 recommendations.
Elsa Valli from UNICEF Innocenti presented her work on anti-poverty programming and IPV in Ghana at the Centre for the study of African Economies conference (Oxford), March 2019.
Leveraging Predictive Models to Reduce ReadmissionsHealth Catalyst
Far too often analytics efforts have fallen short of making a tangible impact on outcomes because they haven’t been successfully implemented in real workflows. Predictive models remain at risk of becoming isolated in their use along the continuum of care where their integration may provide benefits larger than the sum of each silo.
To combat this, UnityPoint Health (UPH) focused on integrating analytical models within the same readmission reduction strategy and coaching the care team to facilitate their adoption. Using this approach, one of UPH hospital’s risk-adjusted readmission indexes improved 40 percent over three years, surpassing internal system targets in performance and becoming the top performer in the health system.
Learning Objectives:
- Describe applicable predictive models useful in reducing 30-day readmissions.
- Learn the elements of a successful readmissions reduction strategy in an integrated health system.
- Understand common obstacles faced in the adoption of analytical tools and how to overcome them.
View this webinar to gain knowledge of the analytics tools and methods UPH used, including innovative individualized risk heat-maps generated for each patient, strategies for analytics adoption, and lessons learned along the way.
The document describes the development of a dashboard to measure the impact of Innovation Units at Massachusetts General Hospital. It outlines the dashboard development process, including selecting metrics, collecting data from various sources, and using visual displays and benchmarks to show performance over time. The goal is to use data to drive improvement through testing changes and spreading improvements. Sample metrics in the dashboard include falls, pressure ulcers, central line infections, and patient and staff satisfaction measures.
Mechanisms for Integrating Real Data into Search Game Simulations: An Applica...Martin Chapman
This document describes using search game models to explore complex public health issues like winter health service pressures. It discusses integrating real demographic and behavioral data into search game simulations to more accurately model these issues. An example application looks at modeling family resource acquisition challenges during winter and evaluating government intervention strategies to reduce pressures and inequalities. Integrating multiple real datasets allows configuring the search game model to replicate observed winter trends and assess intervention performance. The results suggest advising families is most effective at reducing health service utilization and disparities.
This document summarizes research conducted by MKT 8200 on mobile market research startup 1Q. It includes the following key points:
1) 1Q is a mobile market research startup that pays users small amounts to answer survey questions and shares location data with partner companies. The research aims to help 1Q increase active user engagement.
2) Surveys of 1Q users found that intrinsic motivations like having their voice heard are more influential than monetary compensation in driving user engagement. Companies can effectively use surveys for engagement without offering rewards.
3) Analysis of factors influencing users' likelihood to recommend 1Q found that early adopters, those likely to share responses, and satisfied users were most likely to recommend. You
The document discusses using regression for association testing and prediction. It provides an example analyzing factors associated with memory scores in HIV patients. A linear regression found larger household size was associated with higher memory scores when controlling for age and clinic. This provides evidence that household size may contribute to memory loss in HIV patients. The document also discusses using regression for prediction, comparing models, and measures for predictive accuracy like AUC.
Presentation at the European Central Bank, Nov 6, 2013
Panel surveys are used to measure change over time, but previous research has shown that simply asking the same questions of the same respondents in repeated interviews leads to overreporting of change. With proactive dependent interviewing, responses from the previous interview are preloaded into the questionnaire, and respondents are reminded of this information before being asked about their current situation. Existing research has shown that dependent interviewing techniques can reduce spurious change in wave-to-wave reports and thus improve the quality of estimates from longitudinal data. However, the literature provides little guidance on how such questions should be worded. After reminding a respondent of her report in the last wave (“Last time we interviewed you, you said that you were not employed”), we might ask: “Is that still the case?”; “Has that changed?”; “Is that still the case or has that changed?”; or we might ask the original question again: “What is your current labour market activity?”. In this study we present experimental evidence from a longitudinal telephone survey in Germany (n=1500) in which we experimentally manipulated the wording of the dependent questions and contrasted them with independent questions. We report differences in the responses collected by the different question types. Due to the concern that respondents may falsely confirm previous information as still applying, leading to underreporting of change in dependent interviewing, we also test hypotheses about how respondents answer such questions. In these tests, we focus on the roles played by personality, deliberate misreporting to shorten the interview, least effort strategies and cognitive ability in the response process to dependent questions. The paper provides evidence-based guidance on questionnaire design for panel surveys.
joint work in Annette Jaeckle, University of Essex
The document summarizes a student's community health internship project examining car seat safety compliance using the Theory of Planned Behavior framework. Key findings include:
- Rates of rear-facing car seat compliance were high at 87% based on a survey of 110 parents/caregivers.
- Factors like education level, income, and doctor recommendations influenced compliance levels.
- The Theory of Planned Behavior was highly predictive of intentions and behavior, while the Health Belief Model showed no significance with the measures used.
- Further outreach is needed targeting non-compliers, low education/income groups, and emphasizing rear-facing to age 2 recommendations.
Elsa Valli from UNICEF Innocenti presented her work on anti-poverty programming and IPV in Ghana at the Centre for the study of African Economies conference (Oxford), March 2019.
Leveraging Predictive Models to Reduce ReadmissionsHealth Catalyst
Far too often analytics efforts have fallen short of making a tangible impact on outcomes because they haven’t been successfully implemented in real workflows. Predictive models remain at risk of becoming isolated in their use along the continuum of care where their integration may provide benefits larger than the sum of each silo.
To combat this, UnityPoint Health (UPH) focused on integrating analytical models within the same readmission reduction strategy and coaching the care team to facilitate their adoption. Using this approach, one of UPH hospital’s risk-adjusted readmission indexes improved 40 percent over three years, surpassing internal system targets in performance and becoming the top performer in the health system.
Learning Objectives:
- Describe applicable predictive models useful in reducing 30-day readmissions.
- Learn the elements of a successful readmissions reduction strategy in an integrated health system.
- Understand common obstacles faced in the adoption of analytical tools and how to overcome them.
View this webinar to gain knowledge of the analytics tools and methods UPH used, including innovative individualized risk heat-maps generated for each patient, strategies for analytics adoption, and lessons learned along the way.
The document describes the development of a dashboard to measure the impact of Innovation Units at Massachusetts General Hospital. It outlines the dashboard development process, including selecting metrics, collecting data from various sources, and using visual displays and benchmarks to show performance over time. The goal is to use data to drive improvement through testing changes and spreading improvements. Sample metrics in the dashboard include falls, pressure ulcers, central line infections, and patient and staff satisfaction measures.
Mechanisms for Integrating Real Data into Search Game Simulations: An Applica...Martin Chapman
This document describes using search game models to explore complex public health issues like winter health service pressures. It discusses integrating real demographic and behavioral data into search game simulations to more accurately model these issues. An example application looks at modeling family resource acquisition challenges during winter and evaluating government intervention strategies to reduce pressures and inequalities. Integrating multiple real datasets allows configuring the search game model to replicate observed winter trends and assess intervention performance. The results suggest advising families is most effective at reducing health service utilization and disparities.
The C. Everett Koop National Health Award recognizes population health promotion and improvement programs. Each year, awards are presented by The Health Project’s leadership to winning organizations as part of the annual HERO Forum each fall. This Thursday Ron Goetzel joins us for an update on the C. Everett Koop National Health Award with information on criteria and how to apply.
REVIEWpublished 11 June 2018doi 10.3389fpubh.2018.001.docxmalbert5
This document provides an overview of the nine steps involved in developing and validating scales for health, social, and behavioral research. It describes the three phases of scale development: item development, scale development, and scale evaluation. Item development involves identifying domains and generating items, and assessing content validity. Scale development includes pre-testing questions, administering surveys, reducing items, and extracting factors. Scale evaluation consists of testing dimensionality, reliability, and validity. The document aims to concisely review best practices for each step to facilitate the development of rigorous and meaningful scales.
REVIEWpublished 11 June 2018doi 10.3389fpubh.2018.001.docxhealdkathaleen
REVIEW
published: 11 June 2018
doi: 10.3389/fpubh.2018.00149
Frontiers in Public Health | www.frontiersin.org 1 June 2018 | Volume 6 | Article 149
Edited by:
Jimmy Thomas Efird,
University of Newcastle, Australia
Reviewed by:
Aida Turrini,
Consiglio per la Ricerca in Agricoltura
e L’analisi Dell’Economia Agraria
(CREA), Italy
Mary Evelyn Northridge,
New York University, United States
*Correspondence:
Godfred O. Boateng
[email protected]
Specialty section:
This article was submitted to
Epidemiology,
a section of the journal
Frontiers in Public Health
Received: 26 February 2018
Accepted: 02 May 2018
Published: 11 June 2018
Citation:
Boateng GO, Neilands TB,
Frongillo EA, Melgar-Quiñonez HR and
Young SL (2018) Best Practices for
Developing and Validating Scales for
Health, Social, and Behavioral
Research: A Primer.
Front. Public Health 6:149.
doi: 10.3389/fpubh.2018.00149
Best Practices for Developing and
Validating Scales for Health, Social,
and Behavioral Research: A Primer
Godfred O. Boateng1*, Torsten B. Neilands 2, Edward A. Frongillo 3,
Hugo R. Melgar-Quiñonez 4 and Sera L. Young1,5
1 Department of Anthropology and Global Health, Northwestern University, Evanston, IL, United States, 2 Division of
Prevention Science, Department of Medicine, University of California, San Francisco, San Francisco, CA, United States,
3 Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina,
Columbia, SC, United States, 4 Institute for Global Food Security, School of Human Nutrition, McGill University, Montreal, QC,
Canada, 5 Institute for Policy Research, Northwestern University, Evanston, IL, United States
Scale development and validation are critical to much of the work in the health,
social, and behavioral sciences. However, the constellation of techniques required
for scale development and evaluation can be onerous, jargon-filled, unfamiliar, and
resource-intensive. Further, it is often not a part of graduate training. Therefore, our
goal was to concisely review the process of scale development in as straightforward
a manner as possible, both to facilitate the development of new, valid, and reliable
scales, and to help improve existing ones. To do this, we have created a primer for
best practices for scale development in measuring complex phenomena. This is not
a systematic review, but rather the amalgamation of technical literature and lessons
learned from our experiences spent creating or adapting a number of scales over the
past several decades. We identified three phases that span nine steps. In the first phase,
items are generated and the validity of their content is assessed. In the second phase,
the scale is constructed. Steps in scale construction include pre-testing the questions,
administering the survey, reducing the number of items, and understanding how many
factors the scale captures. In the third phase, scale evaluation, the numbe ...
Lincoln Lau, PhD, Director of Research for International Care Ministries discusses the results of a study regarding the relationship between the level of trust in faith leaders and the efficacy of health interventions.
Peter Levesque explores the critical areas of measuring, interpreting, and analyzing results to ensure continual improvement of KT activities to produce intended results.
The document summarizes the results of a 17 question survey about retirement benefits that was completed by 83 people. Key findings include: over 79% of respondents were female, most worked in the public sector, 81% had retirement benefits through their employer, and the most popular solution to address Kentucky's pension underfunding was requiring new employees to enroll in a 401k. The survey methodology, limitations, and references are also outlined.
Propagating Data Policies - A User StudyEnrico Daga
The document summarizes a user study conducted to evaluate a system that propagates data policies in data flows. 10 participant teams were given 5 data journeys involving real datasets and processes to determine what policies should propagate from input to output. The teams used a tool to understand the journeys and compare their decisions to the system. An accuracy analysis was conducted on the results and teams provided feedback through a questionnaire.
ExUM - Invited Talk on Nudging in RecSysAlain Starke
I present work on using explanatory nudges to support 'better' decision-making in recommender systems. I aim to help people to achieve their behavioral goals by providing relevant options in the short-term that are clearly explained to them.
This document outlines a quality improvement project to improve efficiency and patient satisfaction at the emergency room of North Side Hospital. The project aims to decrease length of stay to under 100 minutes, increase patient satisfaction scores to over 75th percentile, and reduce left without being seen rates to under 1%. The document identifies key stakeholders, analyzes current processes and data, and lists interventions to be implemented between July and November 2004 such as new equipment, improved relationships, and enhanced ancillary services. It shows the project achieved significant reductions in length of stay, admissions, and left without being seen rates after initiation.
This document outlines a training curriculum for evaluating the socio-economic impact of a water program. It covers four sessions over four days: introduction and overview, evaluation design, sample design and data collection, and indicators and questionnaire design. Key topics include causal inference, impact evaluation methods like randomized assignment and difference-in-differences, sample designs, and designing indicators and questionnaires. The document uses a case study of Mexico's Progresa anti-poverty program to illustrate concepts like randomized assignment, pre-post comparisons, and enrolled vs non-enrolled comparisons.
Adaptation of Evidence-based Interventions and De-Implementation of Ineffecti...HopkinsCFAR
The document discusses emerging topics in implementation science, including the adaptation of evidence-based interventions and de-implementation of ineffective programs. It provides definitions and concepts for fidelity versus adaptation, and outlines frameworks for understanding how and when adaptations can be made. The document also defines de-implementation and summarizes a portfolio analysis of NIH-funded de-implementation research grants. It concludes that adaptation and de-implementation are emerging areas that require further study to advance implementation science.
SNAP participation is associated with improved self-assessed health and reduced healthcare utilization. Specifically:
1) SNAP increases the probability of reporting excellent or very good health and decreases the probability of reporting good, fair or poor health.
2) SNAP is estimated to reduce the number of sick days per year by 1-2 days on average.
3) SNAP also reduces office-based doctor visits and outpatient visits, with reductions of 1-2 visits per year for office visits and a statistically significant but smaller reduction in outpatient visits.
3 Perspectives to Better Apply Predictive & Prescriptive Models in HealthcareHealth Catalyst
The document discusses 3 perspectives - functional, contextual, and operational understanding - that are important to better apply predictive and prescriptive models in healthcare. Functional understanding means ensuring the model makes clinical sense. Contextual understanding is about how the model fits into clinical workflows and who will act on results. Operational understanding examines the benefits and risks of model deployment, such as sensitivity and predictive value. Providing these three levels of understanding can help support leadership decision making about predictive model deployment as it is more a change management challenge than technical issue.
Purpose of the Call:
•Recap of aggregated MedRec audit month data that identifies potential opportunities for improvement
•Review quality improvement concepts as it relates to measuring for quality improvement
•Hear how Horizon Health team (NB) is using their data to improve MedRec processes
•Receive a tutorial on how to access your MedRec Quality Score run charts in Patient Safety Metrics.
WATCH: http://bit.ly/1EVcREL
This document provides an overview of key concepts in biostatistics. It defines biostatistics as the application of statistical methods in the fields of biology, public health, and medicine. Some key points covered include:
- The types of data: qualitative, quantitative, discrete, continuous
- Descriptive statistics like mean, median, and mode
- Inferential statistics like hypothesis testing and estimating parameters
- Important statistical tests like t-tests, ANOVA, and chi-squared tests
- Measures of diagnostic accuracy like sensitivity, specificity, and predictive values
- The process of determining sample size for studies based on factors like confidence interval, power, and allowable error.
Irish consumer switching intentions and experiences. Read more about the findings here:
https://www.permanenttsb.ie/switch-to-us/switching-index/
And work out your own switcher savings here:
https://www.permanenttsb.ie/switch-to-us/switching-index/are-you-a-power-switcher/
Fitness Tracking Technologies: Data Privacy Doesn’t Matter?Aylin Ilhan
The document discusses a study on perceptions of data privacy and sensitivity related to fitness tracking technologies. The study examined how current, former, and non-users (U, FU, NU) perceive the sensitivity of different types of data collected by fitness trackers. It also looked at general privacy concerns among the three groups. The results found some differences in how health-related data is perceived between users and non-users. Current users were clustered into three groups - concerned, neutral, and unconcerned - based on their data privacy concerns and perceptions of sensitivity. The concerned users saw most data as sensitive while the unconcerned saw very little as sensitive.
Healthier Life and More Fun? Users' Motivations to Apply Activity Tracking Te...Aylin Ilhan
The document discusses physical inactivity and ways to increase physical activity levels using gamification. It presents results from interviews and an online survey of 942 fitness tracker users. Reaction-based notifications that provide step count updates were found to be motivating. Gamification elements like challenges and competitions that allow users to compare progress against others were also found to increase enjoyment and motivate users to be more active. While users were motivated by both intrinsic factors like learning and enjoyment as well as extrinsic rewards, intrinsic motivation had a stronger influence on behavior. The discussion considers how gamification can motivate physical activity but may not directly translate to increased activity levels and whether impacts are sustained over time.
More Related Content
Similar to 10,000 Steps a Day for Health? User-based Evaluation of Wearable Activity Trackers
The C. Everett Koop National Health Award recognizes population health promotion and improvement programs. Each year, awards are presented by The Health Project’s leadership to winning organizations as part of the annual HERO Forum each fall. This Thursday Ron Goetzel joins us for an update on the C. Everett Koop National Health Award with information on criteria and how to apply.
REVIEWpublished 11 June 2018doi 10.3389fpubh.2018.001.docxmalbert5
This document provides an overview of the nine steps involved in developing and validating scales for health, social, and behavioral research. It describes the three phases of scale development: item development, scale development, and scale evaluation. Item development involves identifying domains and generating items, and assessing content validity. Scale development includes pre-testing questions, administering surveys, reducing items, and extracting factors. Scale evaluation consists of testing dimensionality, reliability, and validity. The document aims to concisely review best practices for each step to facilitate the development of rigorous and meaningful scales.
REVIEWpublished 11 June 2018doi 10.3389fpubh.2018.001.docxhealdkathaleen
REVIEW
published: 11 June 2018
doi: 10.3389/fpubh.2018.00149
Frontiers in Public Health | www.frontiersin.org 1 June 2018 | Volume 6 | Article 149
Edited by:
Jimmy Thomas Efird,
University of Newcastle, Australia
Reviewed by:
Aida Turrini,
Consiglio per la Ricerca in Agricoltura
e L’analisi Dell’Economia Agraria
(CREA), Italy
Mary Evelyn Northridge,
New York University, United States
*Correspondence:
Godfred O. Boateng
[email protected]
Specialty section:
This article was submitted to
Epidemiology,
a section of the journal
Frontiers in Public Health
Received: 26 February 2018
Accepted: 02 May 2018
Published: 11 June 2018
Citation:
Boateng GO, Neilands TB,
Frongillo EA, Melgar-Quiñonez HR and
Young SL (2018) Best Practices for
Developing and Validating Scales for
Health, Social, and Behavioral
Research: A Primer.
Front. Public Health 6:149.
doi: 10.3389/fpubh.2018.00149
Best Practices for Developing and
Validating Scales for Health, Social,
and Behavioral Research: A Primer
Godfred O. Boateng1*, Torsten B. Neilands 2, Edward A. Frongillo 3,
Hugo R. Melgar-Quiñonez 4 and Sera L. Young1,5
1 Department of Anthropology and Global Health, Northwestern University, Evanston, IL, United States, 2 Division of
Prevention Science, Department of Medicine, University of California, San Francisco, San Francisco, CA, United States,
3 Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina,
Columbia, SC, United States, 4 Institute for Global Food Security, School of Human Nutrition, McGill University, Montreal, QC,
Canada, 5 Institute for Policy Research, Northwestern University, Evanston, IL, United States
Scale development and validation are critical to much of the work in the health,
social, and behavioral sciences. However, the constellation of techniques required
for scale development and evaluation can be onerous, jargon-filled, unfamiliar, and
resource-intensive. Further, it is often not a part of graduate training. Therefore, our
goal was to concisely review the process of scale development in as straightforward
a manner as possible, both to facilitate the development of new, valid, and reliable
scales, and to help improve existing ones. To do this, we have created a primer for
best practices for scale development in measuring complex phenomena. This is not
a systematic review, but rather the amalgamation of technical literature and lessons
learned from our experiences spent creating or adapting a number of scales over the
past several decades. We identified three phases that span nine steps. In the first phase,
items are generated and the validity of their content is assessed. In the second phase,
the scale is constructed. Steps in scale construction include pre-testing the questions,
administering the survey, reducing the number of items, and understanding how many
factors the scale captures. In the third phase, scale evaluation, the numbe ...
Lincoln Lau, PhD, Director of Research for International Care Ministries discusses the results of a study regarding the relationship between the level of trust in faith leaders and the efficacy of health interventions.
Peter Levesque explores the critical areas of measuring, interpreting, and analyzing results to ensure continual improvement of KT activities to produce intended results.
The document summarizes the results of a 17 question survey about retirement benefits that was completed by 83 people. Key findings include: over 79% of respondents were female, most worked in the public sector, 81% had retirement benefits through their employer, and the most popular solution to address Kentucky's pension underfunding was requiring new employees to enroll in a 401k. The survey methodology, limitations, and references are also outlined.
Propagating Data Policies - A User StudyEnrico Daga
The document summarizes a user study conducted to evaluate a system that propagates data policies in data flows. 10 participant teams were given 5 data journeys involving real datasets and processes to determine what policies should propagate from input to output. The teams used a tool to understand the journeys and compare their decisions to the system. An accuracy analysis was conducted on the results and teams provided feedback through a questionnaire.
ExUM - Invited Talk on Nudging in RecSysAlain Starke
I present work on using explanatory nudges to support 'better' decision-making in recommender systems. I aim to help people to achieve their behavioral goals by providing relevant options in the short-term that are clearly explained to them.
This document outlines a quality improvement project to improve efficiency and patient satisfaction at the emergency room of North Side Hospital. The project aims to decrease length of stay to under 100 minutes, increase patient satisfaction scores to over 75th percentile, and reduce left without being seen rates to under 1%. The document identifies key stakeholders, analyzes current processes and data, and lists interventions to be implemented between July and November 2004 such as new equipment, improved relationships, and enhanced ancillary services. It shows the project achieved significant reductions in length of stay, admissions, and left without being seen rates after initiation.
This document outlines a training curriculum for evaluating the socio-economic impact of a water program. It covers four sessions over four days: introduction and overview, evaluation design, sample design and data collection, and indicators and questionnaire design. Key topics include causal inference, impact evaluation methods like randomized assignment and difference-in-differences, sample designs, and designing indicators and questionnaires. The document uses a case study of Mexico's Progresa anti-poverty program to illustrate concepts like randomized assignment, pre-post comparisons, and enrolled vs non-enrolled comparisons.
Adaptation of Evidence-based Interventions and De-Implementation of Ineffecti...HopkinsCFAR
The document discusses emerging topics in implementation science, including the adaptation of evidence-based interventions and de-implementation of ineffective programs. It provides definitions and concepts for fidelity versus adaptation, and outlines frameworks for understanding how and when adaptations can be made. The document also defines de-implementation and summarizes a portfolio analysis of NIH-funded de-implementation research grants. It concludes that adaptation and de-implementation are emerging areas that require further study to advance implementation science.
SNAP participation is associated with improved self-assessed health and reduced healthcare utilization. Specifically:
1) SNAP increases the probability of reporting excellent or very good health and decreases the probability of reporting good, fair or poor health.
2) SNAP is estimated to reduce the number of sick days per year by 1-2 days on average.
3) SNAP also reduces office-based doctor visits and outpatient visits, with reductions of 1-2 visits per year for office visits and a statistically significant but smaller reduction in outpatient visits.
3 Perspectives to Better Apply Predictive & Prescriptive Models in HealthcareHealth Catalyst
The document discusses 3 perspectives - functional, contextual, and operational understanding - that are important to better apply predictive and prescriptive models in healthcare. Functional understanding means ensuring the model makes clinical sense. Contextual understanding is about how the model fits into clinical workflows and who will act on results. Operational understanding examines the benefits and risks of model deployment, such as sensitivity and predictive value. Providing these three levels of understanding can help support leadership decision making about predictive model deployment as it is more a change management challenge than technical issue.
Purpose of the Call:
•Recap of aggregated MedRec audit month data that identifies potential opportunities for improvement
•Review quality improvement concepts as it relates to measuring for quality improvement
•Hear how Horizon Health team (NB) is using their data to improve MedRec processes
•Receive a tutorial on how to access your MedRec Quality Score run charts in Patient Safety Metrics.
WATCH: http://bit.ly/1EVcREL
This document provides an overview of key concepts in biostatistics. It defines biostatistics as the application of statistical methods in the fields of biology, public health, and medicine. Some key points covered include:
- The types of data: qualitative, quantitative, discrete, continuous
- Descriptive statistics like mean, median, and mode
- Inferential statistics like hypothesis testing and estimating parameters
- Important statistical tests like t-tests, ANOVA, and chi-squared tests
- Measures of diagnostic accuracy like sensitivity, specificity, and predictive values
- The process of determining sample size for studies based on factors like confidence interval, power, and allowable error.
Irish consumer switching intentions and experiences. Read more about the findings here:
https://www.permanenttsb.ie/switch-to-us/switching-index/
And work out your own switcher savings here:
https://www.permanenttsb.ie/switch-to-us/switching-index/are-you-a-power-switcher/
Fitness Tracking Technologies: Data Privacy Doesn’t Matter?Aylin Ilhan
The document discusses a study on perceptions of data privacy and sensitivity related to fitness tracking technologies. The study examined how current, former, and non-users (U, FU, NU) perceive the sensitivity of different types of data collected by fitness trackers. It also looked at general privacy concerns among the three groups. The results found some differences in how health-related data is perceived between users and non-users. Current users were clustered into three groups - concerned, neutral, and unconcerned - based on their data privacy concerns and perceptions of sensitivity. The concerned users saw most data as sensitive while the unconcerned saw very little as sensitive.
Healthier Life and More Fun? Users' Motivations to Apply Activity Tracking Te...Aylin Ilhan
The document discusses physical inactivity and ways to increase physical activity levels using gamification. It presents results from interviews and an online survey of 942 fitness tracker users. Reaction-based notifications that provide step count updates were found to be motivating. Gamification elements like challenges and competitions that allow users to compare progress against others were also found to increase enjoyment and motivate users to be more active. While users were motivated by both intrinsic factors like learning and enjoyment as well as extrinsic rewards, intrinsic motivation had a stronger influence on behavior. The discussion considers how gamification can motivate physical activity but may not directly translate to increased activity levels and whether impacts are sustained over time.
Motivations to Join Fitness Communities on Facebook: Which Gratifications Are...Aylin Ilhan
Presentation at the 20th International Conference on Human-Computer Interaction in Las Vegas, Nevada
Thematic Area: Social Computing and Social Media
Session: Fitness, Health, and Wearables - Activity Trackers in the Everyday Life.
Bicycle Sharing System in the Smart City BarcelonaAylin Ilhan
An evaluation of the bicycle sharing system in the smart city Barcelona presented at the international Library and Information Science Conference (LIS) 2017 in Sapporo, Japan.
Do Car Drivers Really Need Mobile Parking Payment? A Critical Evaluation of t...Aylin Ilhan
Presentation at the 19th International Conference on Human-Computer Interaction in Vancouver, Canada
Thematic Area: Design, User Experience and Usability
Thematic Subarea: Quality of Service in IT
User behaviour in the Twittersphere: Content analysis of tweets on Charlie He...Aylin Ilhan
The document summarizes a study analyzing tweets related to the 2015 Charlie Hebdo attacks. The study analyzed over 7,000 tweets collected over a one week period. Key findings included that 44% of tweets contained links, with 51% of links going to news sites. 31% of tweets included multimedia. Hashtags expressing solidarity, like #JeSuisCharlie, were most common. Embedding multimedia in tweets was found to correlate with higher retweet counts on most days. Links to news sites decreased later in the week as information spread, while links to social media increased. The discussion suggests tweets with multimedia and links to news spread information faster during emergencies.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"
10,000 Steps a Day for Health? User-based Evaluation of Wearable Activity Trackers
1. 10,000 Steps a Day for Health?
User-based Evaluation of Wearable Activity Trackers
Aylin Ilhan and Maria Henkel
Department of Information Science
Heinrich Heine University Düsseldorf, Germany
HICSS-51, 03. – 06. January 2018, Hilton Waikoloa Village
18. 9
D2:
User
D1:
Perceived Service Quality
D3:
Service Acceptance
RQ1a: What strengths and weaknesses are recognized by the participants (D1 & D3)?
Ease of Use
Usefulness
Trust
Fun
Gamification
Opting-Out
Medical Health Funds Reduce Medical Costs
Use
Impact
Dissemination
Contagion
Group Pressure
Enforcement
19. 10
D2:
User
D1:
Perceived Service Quality
D3:
Service Acceptance
RQ1b: How do perceived service quality and acceptance of activity trackers
influence each other (D1 & D3)?
Ease of Use
Usefulness
Trust
Fun
Gamification
Opting-Out
Medical Health Funds Reduce Medical Costs
Use
Impact
Dissemination
Contagion
Group Pressure
Enforcement
20. 11
D2:
User
D1:
Perceived Service Quality
D3:
Service Acceptance
RQ2: Country-specific differences based on perceived service quality and acceptance,
regarding activity trackers (D1& D3)?
Ease of Use
Usefulness
Trust
Fun
Gamification
Opting-Out
Medical Health Funds Reduce Medical Costs
Use
Impact
Dissemination
Contagion
Group Pressure
Enforcement
21. 12
D2:
User
D1:
Perceived Service Quality
D3:
Service Acceptance
RQ3: Country-specific differences based on medical health funds and reducing medical
costs, regarding sharing activity data?
Ease of Use
Usefulness
Trust
Fun
Gamification
Use Opting-Out
Impact
Dissemination
Contagion
Group Pressure
Enforcement
Medical Health Funds Reduce Medical Costs
51. Next steps
34
• Understanding of country-specific differences related to medical
health insurances requires more American and international participants
• Deeper analysis of different medical health insurance funds
• Qualitative interviews
• Analysis of different generations (Generation X, Generation Y, Generation Z,
Baby boomers)