This talk, about patient and public involvement in research, was delivered at the inaugural co-developed conference at University College Dublin, titled 'A collaborative approach to arthritis and rheumatic disease research'.
This document discusses the concepts of association and causation in epidemiology. It defines association as the occurrence of two variables more often than expected by chance. Causation requires that one factor leads to a change in another factor. Several types of associations are described, including direct, indirect, spurious and causal relationships. Guidelines for determining if an association is likely causal include temporal relationship, strength of association, dose-response relationship, replication of findings, biological plausibility and consideration of alternative explanations. Models of causation like the epidemiological triad, web of causation and Rothman's component causes model are also summarized.
Sample Size Calculation for Quantitive and Qualitative Studies Akmal Samsor
This document discusses sample size calculations for research studies. It covers the formula for calculating sample size for infinite populations, how to modify the sample size by adding a design effect and non-response rate. It also discusses using online calculators and calculating sample size for finite populations and qualitative studies. Participants will learn about sample size calculations and practice calculating sample size for different research studies varying confidence levels, proportions, precision levels, design effects, and non-response rates. The document emphasizes that saturation is key for qualitative sample size but some common rules used are 5 key informant interviews, 30 in-depth interviews, and 1 focus group per group.
This document discusses types of bias that can occur in epidemiological studies. It defines bias as a systematic error that can lead to conclusions that differ from the truth. The main types of bias discussed are selection bias, information bias, and confounding. Selection bias occurs when the study sample is not representative of the target population. Information bias relates to errors in measuring exposures or outcomes. Confounding occurs when an independent variable other than the exposure of interest influences the outcome. The document provides numerous examples of biases that can arise in specific study designs such as case-control, cohort, and cross-sectional studies.
This document discusses ethics in medical research. It begins by outlining the lesson objectives which are to explain ethics, describe important historical events related to research ethics, list important guidelines, describe informed consent, and describe the role of institutional ethics committees. It then discusses the definition of ethics, important historical incidents like the Nuremberg trials, Thalidomide tragedy, and Tuskegee syphilis experiment. It also describes key documents that outline research ethics guidelines like the Nuremberg Code, Helsinki Declaration, ICH Guidelines, and ICMR Guidelines. It concludes by explaining informed consent and the responsibilities of institutional ethics committees in research.
This document discusses types of variables and biases in observational studies. It defines categorical, confounding, continuous, control, dependent, discrete, independent, nominal, ordinal, qualitative, and quantitative variables. Selection bias, information bias, and confounding are described as common types of bias in observational designs. Selection bias results from a lack of comparability between study groups. Information bias occurs from incorrect determination of exposure or outcomes. Confounding involves external factors blurring the effect of the exposure being studied. Methods to address confounding include restriction, matching, stratification, and multivariate analysis techniques.
Validity and bias in epidemiological studyAbhijit Das
Validity and bias are essential aspects of any research—a brief description of internal and external validity and different types of bias related to the epidemiological study.
This document discusses the concepts of association and causation in epidemiology. It defines association as the occurrence of two variables more often than expected by chance. Causation requires that one factor leads to a change in another factor. Several types of associations are described, including direct, indirect, spurious and causal relationships. Guidelines for determining if an association is likely causal include temporal relationship, strength of association, dose-response relationship, replication of findings, biological plausibility and consideration of alternative explanations. Models of causation like the epidemiological triad, web of causation and Rothman's component causes model are also summarized.
Sample Size Calculation for Quantitive and Qualitative Studies Akmal Samsor
This document discusses sample size calculations for research studies. It covers the formula for calculating sample size for infinite populations, how to modify the sample size by adding a design effect and non-response rate. It also discusses using online calculators and calculating sample size for finite populations and qualitative studies. Participants will learn about sample size calculations and practice calculating sample size for different research studies varying confidence levels, proportions, precision levels, design effects, and non-response rates. The document emphasizes that saturation is key for qualitative sample size but some common rules used are 5 key informant interviews, 30 in-depth interviews, and 1 focus group per group.
This document discusses types of bias that can occur in epidemiological studies. It defines bias as a systematic error that can lead to conclusions that differ from the truth. The main types of bias discussed are selection bias, information bias, and confounding. Selection bias occurs when the study sample is not representative of the target population. Information bias relates to errors in measuring exposures or outcomes. Confounding occurs when an independent variable other than the exposure of interest influences the outcome. The document provides numerous examples of biases that can arise in specific study designs such as case-control, cohort, and cross-sectional studies.
This document discusses ethics in medical research. It begins by outlining the lesson objectives which are to explain ethics, describe important historical events related to research ethics, list important guidelines, describe informed consent, and describe the role of institutional ethics committees. It then discusses the definition of ethics, important historical incidents like the Nuremberg trials, Thalidomide tragedy, and Tuskegee syphilis experiment. It also describes key documents that outline research ethics guidelines like the Nuremberg Code, Helsinki Declaration, ICH Guidelines, and ICMR Guidelines. It concludes by explaining informed consent and the responsibilities of institutional ethics committees in research.
This document discusses types of variables and biases in observational studies. It defines categorical, confounding, continuous, control, dependent, discrete, independent, nominal, ordinal, qualitative, and quantitative variables. Selection bias, information bias, and confounding are described as common types of bias in observational designs. Selection bias results from a lack of comparability between study groups. Information bias occurs from incorrect determination of exposure or outcomes. Confounding involves external factors blurring the effect of the exposure being studied. Methods to address confounding include restriction, matching, stratification, and multivariate analysis techniques.
Validity and bias in epidemiological studyAbhijit Das
Validity and bias are essential aspects of any research—a brief description of internal and external validity and different types of bias related to the epidemiological study.
A cohort study examines the effect of exposures on a group of subjects over time by collecting data on exposures and outcomes. It has several advantages: the temporal sequence between exposure and outcome provides evidence of causality, multiple outcomes can be examined, and rare exposures can be studied. However, cohort studies are costly, prone to dropout, and require large sample sizes when studying rare outcomes. They also cannot prove causality definitively.
This document discusses assessing risk of bias during systematic reviews. It defines bias as systematic error that deviates from the truth and can lead to over or underestimating effects. Assessing bias in included studies is important because results may be consistent due to flaws. There are seven domains for assessing bias: selection, performance, detection, attrition, reporting, and other biases. Risk of bias is assessed by reviewing study methods, looking for missing information, and making judgments on pre-specified criteria about the likelihood studies were affected by bias in each domain. Tools like risk of bias tables are used to categorize judgments of low, high, or unclear risk of bias in individual studies.
This document discusses case-control studies. It begins with an introduction and definition of case-control studies. It then covers the basic steps in conducting a case-control study, including estimating sample size, measures of association, and potential biases. Key points include that case-control studies are retrospective and compare exposures between cases and controls to determine associations with outcomes. Odds ratios are commonly used to measure associations while potential biases include recall and selection biases.
- Randomized controlled trials (RCTs) are experiments in which people are randomly allocated to different intervention groups in order to evaluate the effects of those interventions.
- RCTs help reduce bias and allow for comparisons between groups that are otherwise similar. Random allocation means each participant has an equal chance of being placed in any group.
- RCTs involve an experimental group that receives the new intervention being tested and a control group that receives an alternative treatment, no treatment, or a placebo. Comparing outcomes between the groups allows researchers to determine the efficacy of the intervention.
This document provides definitions and concepts related to biostatistics. It defines key terms like population, sample, variables, and data. It describes qualitative and quantitative data, including categorical data types like nominal and ordinal. It also discusses numerical data presentation methods like frequency distributions, percentages, proportions, ratios, and rates. Measures of central tendency like mean, median, and mode are explained. The document concludes with definitions of measures of variation, the normal distribution, and additional measures of dispersion.
How To Read A Medical Paper: Part 1, Is This a Good Paper?DrLukeKane
The document provides an overview on how to read and evaluate academic papers. It discusses the typical structure of papers, known as IMRAD (Introduction, Methods, Results, Discussion). It outlines factors to consider in determining if a paper is worth reading such as the study design presented in the methods section. The document describes how to critically appraise papers by considering why the study was done, what type of study design was used, and if the design was appropriate. It also reviews hierarchies of evidence, common terms, the peer review process, and reasons why papers may be rejected.
Concept of Association, Causation and Correlation
Association - Spurious, Indirect & Direct
Multi-factorial causation
Guidelines for Judging causality
Additional Criteria for Judging causality
Superiority, Equivalence, and Non-Inferiority Trial DesignsKevin Clauson
http://bit.ly/bQKcGz This lecture was presented as part of the Drug Literature Evaluation course at Nova Southeastern University. Guided notes and an audience response system were used to augment to lecture. Context for my decision to share these slides can be found at the provided link.
This document discusses bias and validity in clinical research. It defines clinical epidemiology as the study of health-related states and events in populations to control health problems. It describes how epidemiologic studies compare outcomes like disease rates between exposed and unexposed groups. Validity is important, with internal validity indicating good construct free from bias/errors, and external validity showing generalizability. Bias and confounding can threaten validity and lead to erroneous associations if not avoided or controlled for.
Evidence Synthesis for Sparse Evidence Base, Heterogeneous Studies, and Disco...InsideScientific
Standard models in evidence synthesis work well in settings characterized by a large evidence base, the absence of effect modifiers, and connected networks. Handling sparse data, substantial between-study heterogeneity and disconnected studies, however, poses challenges to researchers and requires advanced methodology.
In the absence of head-to-head studies, evidence synthesis is a well-established technique to indirectly compare novel and established interventions in various disease areas. In standard settings, the most established methods for various outcome types work well and result in realistic effect estimates. However, there are a variety of situations when standard methods may no longer be sufficient:
- if there is only a sparse network of evidence
- if there is a large amount of between-study heterogeneity
- if the network is disconnected
Key Topics Include:
- General introduction into the objectives of conducting evidence synthesis
- Description of typical situations of “non-standard” data, including sparse networks of evidence, a large amount of between-study heterogeneity, or disconnected networks
- Advanced methods to address non-standard data, including the use of informative priors, subgroup analyses, meta-regression and multi-level meta regression, and matching-adjusted indirect comparisons (MAICs)
- Case studies illustrating how these advanced methods of evidence synthesis are applied on actual data
The document discusses methods for calculating sample sizes for various study designs, including measuring prevalence, cross-sectional studies, case-control studies, and clinical trials. It provides formulas and examples for calculating sample sizes needed to measure a dichotomous outcome and a continuous outcome. For measuring prevalence, the sample size depends on the expected prevalence rate, desired precision level, and confidence interval. For studies comparing two groups, the sample size depends on the event rates in each group and the desired power and significance level to detect a difference between groups.
This document provides an overview of case-control studies, including:
- Case-control studies compare characteristics of people with a disease (cases) to people without the disease (controls) to identify potential risk factors.
- Key components include clearly defining the disease, selecting representative cases and controls, measuring exposures that occurred before disease onset, and accounting for potential confounding factors.
- The odds ratio is used to analyze if cases had higher or lower odds of exposure compared to controls, indicating increased or decreased risk.
The document discusses several issues in medical ethics raised by new technologies, including who decides whether a patient lives or dies, the boundaries of medical research, and obtaining informed consent. It notes that medical research and patient care have different standards for consent. Key considerations for ethical medical research are obtaining informed consent from subjects, properly assessing risks and benefits, and fair selection of subjects without biases or marginalization. The document also discusses opinions on medical futility, emergency treatment without consent, and allowing clinical trials.
Clinical epidemiology investigates and controls the distribution and determinants of disease. It seeks to answer clinical questions and guide clinical decisions using evidence. Clinical epidemiology uses epidemiological methods to make predictions about individual patients by studying outcomes in similar patients groups. It aims to develop valid conclusions and avoid bias or chance influencing clinical observations and interpretations. Key elements include expressing probabilities for individual patients based on past groups, accounting for systematic errors and chance in observations, and relying on scientifically sound principles.
This presentation was delivered as part of a seminar to the Child Health Evaluative Sciences (CHES) Research Group, based at The Hospital for Sick Children (SickKids) in Toronto, ON, Canada. The presentation focused on the importance and some of the practicalities of involving young people in research.
In the driving seat: Health care and research led for, and by young peopleSimon R. Stones
This seminar was delivered as part of the University of Central Lancashire (UCLAN) Centre for Children and Young People’s Participation Seminar Series.
A cohort study examines the effect of exposures on a group of subjects over time by collecting data on exposures and outcomes. It has several advantages: the temporal sequence between exposure and outcome provides evidence of causality, multiple outcomes can be examined, and rare exposures can be studied. However, cohort studies are costly, prone to dropout, and require large sample sizes when studying rare outcomes. They also cannot prove causality definitively.
This document discusses assessing risk of bias during systematic reviews. It defines bias as systematic error that deviates from the truth and can lead to over or underestimating effects. Assessing bias in included studies is important because results may be consistent due to flaws. There are seven domains for assessing bias: selection, performance, detection, attrition, reporting, and other biases. Risk of bias is assessed by reviewing study methods, looking for missing information, and making judgments on pre-specified criteria about the likelihood studies were affected by bias in each domain. Tools like risk of bias tables are used to categorize judgments of low, high, or unclear risk of bias in individual studies.
This document discusses case-control studies. It begins with an introduction and definition of case-control studies. It then covers the basic steps in conducting a case-control study, including estimating sample size, measures of association, and potential biases. Key points include that case-control studies are retrospective and compare exposures between cases and controls to determine associations with outcomes. Odds ratios are commonly used to measure associations while potential biases include recall and selection biases.
- Randomized controlled trials (RCTs) are experiments in which people are randomly allocated to different intervention groups in order to evaluate the effects of those interventions.
- RCTs help reduce bias and allow for comparisons between groups that are otherwise similar. Random allocation means each participant has an equal chance of being placed in any group.
- RCTs involve an experimental group that receives the new intervention being tested and a control group that receives an alternative treatment, no treatment, or a placebo. Comparing outcomes between the groups allows researchers to determine the efficacy of the intervention.
This document provides definitions and concepts related to biostatistics. It defines key terms like population, sample, variables, and data. It describes qualitative and quantitative data, including categorical data types like nominal and ordinal. It also discusses numerical data presentation methods like frequency distributions, percentages, proportions, ratios, and rates. Measures of central tendency like mean, median, and mode are explained. The document concludes with definitions of measures of variation, the normal distribution, and additional measures of dispersion.
How To Read A Medical Paper: Part 1, Is This a Good Paper?DrLukeKane
The document provides an overview on how to read and evaluate academic papers. It discusses the typical structure of papers, known as IMRAD (Introduction, Methods, Results, Discussion). It outlines factors to consider in determining if a paper is worth reading such as the study design presented in the methods section. The document describes how to critically appraise papers by considering why the study was done, what type of study design was used, and if the design was appropriate. It also reviews hierarchies of evidence, common terms, the peer review process, and reasons why papers may be rejected.
Concept of Association, Causation and Correlation
Association - Spurious, Indirect & Direct
Multi-factorial causation
Guidelines for Judging causality
Additional Criteria for Judging causality
Superiority, Equivalence, and Non-Inferiority Trial DesignsKevin Clauson
http://bit.ly/bQKcGz This lecture was presented as part of the Drug Literature Evaluation course at Nova Southeastern University. Guided notes and an audience response system were used to augment to lecture. Context for my decision to share these slides can be found at the provided link.
This document discusses bias and validity in clinical research. It defines clinical epidemiology as the study of health-related states and events in populations to control health problems. It describes how epidemiologic studies compare outcomes like disease rates between exposed and unexposed groups. Validity is important, with internal validity indicating good construct free from bias/errors, and external validity showing generalizability. Bias and confounding can threaten validity and lead to erroneous associations if not avoided or controlled for.
Evidence Synthesis for Sparse Evidence Base, Heterogeneous Studies, and Disco...InsideScientific
Standard models in evidence synthesis work well in settings characterized by a large evidence base, the absence of effect modifiers, and connected networks. Handling sparse data, substantial between-study heterogeneity and disconnected studies, however, poses challenges to researchers and requires advanced methodology.
In the absence of head-to-head studies, evidence synthesis is a well-established technique to indirectly compare novel and established interventions in various disease areas. In standard settings, the most established methods for various outcome types work well and result in realistic effect estimates. However, there are a variety of situations when standard methods may no longer be sufficient:
- if there is only a sparse network of evidence
- if there is a large amount of between-study heterogeneity
- if the network is disconnected
Key Topics Include:
- General introduction into the objectives of conducting evidence synthesis
- Description of typical situations of “non-standard” data, including sparse networks of evidence, a large amount of between-study heterogeneity, or disconnected networks
- Advanced methods to address non-standard data, including the use of informative priors, subgroup analyses, meta-regression and multi-level meta regression, and matching-adjusted indirect comparisons (MAICs)
- Case studies illustrating how these advanced methods of evidence synthesis are applied on actual data
The document discusses methods for calculating sample sizes for various study designs, including measuring prevalence, cross-sectional studies, case-control studies, and clinical trials. It provides formulas and examples for calculating sample sizes needed to measure a dichotomous outcome and a continuous outcome. For measuring prevalence, the sample size depends on the expected prevalence rate, desired precision level, and confidence interval. For studies comparing two groups, the sample size depends on the event rates in each group and the desired power and significance level to detect a difference between groups.
This document provides an overview of case-control studies, including:
- Case-control studies compare characteristics of people with a disease (cases) to people without the disease (controls) to identify potential risk factors.
- Key components include clearly defining the disease, selecting representative cases and controls, measuring exposures that occurred before disease onset, and accounting for potential confounding factors.
- The odds ratio is used to analyze if cases had higher or lower odds of exposure compared to controls, indicating increased or decreased risk.
The document discusses several issues in medical ethics raised by new technologies, including who decides whether a patient lives or dies, the boundaries of medical research, and obtaining informed consent. It notes that medical research and patient care have different standards for consent. Key considerations for ethical medical research are obtaining informed consent from subjects, properly assessing risks and benefits, and fair selection of subjects without biases or marginalization. The document also discusses opinions on medical futility, emergency treatment without consent, and allowing clinical trials.
Clinical epidemiology investigates and controls the distribution and determinants of disease. It seeks to answer clinical questions and guide clinical decisions using evidence. Clinical epidemiology uses epidemiological methods to make predictions about individual patients by studying outcomes in similar patients groups. It aims to develop valid conclusions and avoid bias or chance influencing clinical observations and interpretations. Key elements include expressing probabilities for individual patients based on past groups, accounting for systematic errors and chance in observations, and relying on scientifically sound principles.
This presentation was delivered as part of a seminar to the Child Health Evaluative Sciences (CHES) Research Group, based at The Hospital for Sick Children (SickKids) in Toronto, ON, Canada. The presentation focused on the importance and some of the practicalities of involving young people in research.
In the driving seat: Health care and research led for, and by young peopleSimon R. Stones
This seminar was delivered as part of the University of Central Lancashire (UCLAN) Centre for Children and Young People’s Participation Seminar Series.
Ensuring research really does involve the young personSimon R. Stones
This presentation was delivered during a session discussing the ethics of conducting research with children and young people. The presentation emphasises the importance of involving children, young people and their families in the design and conduct of research, in order to make it more relevant.
Tell me and I forget, teach me and I remember, involve me and I learnSimon R. Stones
This presentation was delivered at the Glasgow Caledonian University School of Health and Life Sciences Research Seminar, to help inform the group who are currently developing their strategy for patient and public involvement and engagement.
Patient and Public Involvement in Research: From Rhetoric To RealityMarie Ennis-O'Connor
It’s an exciting time in health research. As a broader view of what constitutes expertise and research evolves, barriers between the research community and the public are eroding, paving the way for the growth of patient and public involvement (PPI) in research.
PPI occurs when individuals meaningfully and actively collaborate in the governance, priority setting, and conduct of research, as well as in summarizing, distributing, sharing, and applying its resulting knowledge. PPI is an important step in ensuring that the real life experiences of patients are considered in decision-making processes around research.
Co-design, Co-produce, Co-deliver: Collaboration is the only viable path to s...Simon R. Stones
In this presentation, delivered to the Translate external advisory board at their bi-annual meeting, the importance of patient and public involvement in research is highlighted, as well as simple strategies that researchers, healthcare professionals and private organisations can take to involve people in all aspects of research, from the bench to the bedside.
This guide provides scientists with advice on how to effectively communicate science and make their stories more relatable to non-expert audiences. It discusses the importance of science communication and making science understandable to the public. The document outlines 5 ways to tell captivating stories, including putting a human face on your work, creating dramatic tension, connecting with your audience, using concise and meaningful details, and giving an authentic delivery. It then discusses applying these storytelling techniques to common science communication challenges. The overall document aims to empower scientists to communicate their work and stories on a wider scale.
Getting to grips with involving and engaging children, young people and famil...Simon R. Stones
This presentation was delivered to the Leeds Children's Hospital Research Forum, where the practicalities of involving and engaging children, young people and families in research was discussed.
This document discusses framing and how it can be used for effective communication. It provides examples of research conducted by the FrameWorks Institute to identify useful frames to explain complex social issues to different audiences. Some key points:
- Framing involves emphasizing certain aspects of an issue and leaving other things unsaid to shape understanding.
- Cultural models, like shared assumptions, influence how audiences interpret messages. Effective frames activate productive existing models.
- FrameWorks has studied how to discuss issues like early childhood development, health inequalities, and addiction with the public and professionals using framing.
- When a frame "works" it can shift audiences' knowledge, attitudes, and support on an issue by linking
Presentation by Simon Denegri (NIHR) and Jennifer Preston (MCRN) to the Nuffi...Simon Denegri
The document discusses public involvement in health research and young people. It notes that the National Institute for Health Research (NIHR) sees public involvement as a core principle and has invested millions to support involvement. It provides a working definition of involvement as research being carried out "with" or "by" the public rather than "to", "about", or "for" them. The document also highlights the value involvement can add to ensuring research questions are relevant and outcomes are accessible and useful. It cites examples of involvement contributing to priority setting, study design and delivery, and review of research.
This document provides an introduction to research fundamentals for activists. It discusses key concepts like quantitative and qualitative research, research ethics, study designs and interpreting results. The goal is to build activists' research literacy so they can engage in evidence-based advocacy. Some highlights include:
- Community advisory boards can help ensure research addresses community priorities and concerns.
- Quantitative research uses numerical data and closed-ended questions, while qualitative explores beliefs and experiences through open-ended questions. Both have pros and cons depending on the question.
- HIV activists have a long history of using scientific evidence to inform their advocacy agenda and influence research agendas to better address their communities' needs.
- Research ethics principles like respect,
Professional use of social media in medical education - 2014Pat Rich
This document discusses the professional use of social media in medical education. It begins by outlining the lecture objectives to discuss the potentials of social media tools like Facebook and Twitter in medical education and discuss safe and professional behaviors. It then introduces the presenters and their experience with social media in healthcare. The document discusses how social media can provide opportunities for health education, patient support, advocacy, research, and clinical care. However, it also notes challenges like impact on patients, liability, privacy, ethics, and reputation. It provides case studies and guidelines on maintaining professionalism when using social media. In general, it advocates for medical students and physicians to consider social media as learning tools but to always maintain privacy, confidentiality, and appropriate boundaries
The patient and physician interaction in social mediaSimon R. Stones
This presentation was delivered in the 'Tweet up: Social media in rheumatology' session during the British Society of Rheumatology Annual Conference on Tuesday 30 April 2019, in Birmingham, UK.
The document discusses the relationship between corporate social responsibility (CSR), applied research ethics, and the clinical research enterprise. It summarizes views on renegotiating the social contract between industry and society to align private profits with public health interests using a triple bottom line approach of sustainability. The document also discusses how CSR principles of business ethics, sustainability, and governance can translate to clinical research by considering applied research ethics, feasibility of studies, and accountability.
Breaking barriers, embracing expertise: When patients become people in researchSimon R. Stones
This presentation was delivered during the 'Participation in healthcare settings' session of the 6th Children's Research Network for Ireland and Northern Ireland Conference.
Similar to Patient and public involvement in research: Two sides of the same coin (20)
This presentation was delivered at NIHR INVOLVE Diversity and Inclusion Working Group meeting on Tuesday 02 April 2019 in London, England, United Kingdom.
Sesquipedalians versus Hippopotomonstrosesquipedaliophobes: It’s time to make...Simon R. Stones
This presentation was delivered at the 8th European Meeting of ISMPP 2019, as part of a panel discussion titled: Maintaining Our Core Strength: Driving Publication Integrity Through Leadership
This presentation was delivered to parents/carers attending the Children's Chronic Arthritis Association (CCAA) family support weekend on 29 September 2018.
This presentation was delivered to international delegates, consisting of children, young people, families, young person advisory group leaders and industry representatives at the 4th iCAN Research and Advocacy Summit in Edinburgh, UK on Tuesday 10 July 2018.
Stumbling through the fog: A lived experience of fibromyalgiaSimon R. Stones
This presentation was delivered as part of the ME, CFS and Fibromyalgia Alliance Malta VO/818 and European Network of Fibromyalgia Associations (ENFA) Conference held on Saturday 12 May 2018 in Attard, Malta. The conference was opened by Her Excellency, President Marie Louise Coleiro Preca.
This presentation was delivered to members of the NRAS Bolton Rheumatoid Arthritis Support Group (BRASG) at their monthly meeting held in Bolton, United Kingdom.
Your voice, your story, your life: You matterSimon R. Stones
This presentation was delivered as part of the inaugural meeting of the NIHR Manchester Clinical Research Facility Young Person’s Advisory Group, to provide young people with some context about how young people have become involved in research, and the important role that they play in shaping research, care and treatment for the future.
This presentation was delivered as part of a workshop on social media in research at the 6th Children's Research Network for Ireland and Northern Ireland Conference.
The INVOLVE at 21 Conference was held in Westminster, London on Tuesday 28 November 2017. During the opening plenary session, I gave a short talk about the importance of research in modern day medicine, and the journey that I have taken as a result of research.
Paving the way for a brighter future: Opportunities to involve young people ...Simon R. Stones
A presentation delivered at The University of Manchester's Child Health Research Network's workshop on devolution in Greater Manchester. The aim of the overall workshop was to explore the implications and opportunities for child health and wellbeing research in Greater Manchester. Here, I discussed the importance of involving young people and their families in co-designing services and research.
Social media and the path to empowerment: We’ve got the power!Simon R. Stones
Invited speaker presentation about social media, technology and patient empowerment, delivered during the August support group meeting of the Carion Fenn Foundation in Ajax, ON, Canada.
When personal and professional experiences intersect: the path to successSimon R. Stones
This session was delivered to participants at the 1st European Patients' Forum Summer Training Course for Young Patient Advocates. The aim of the session was to share experiences of advocacy, by describing what drives and motivates young advocates. Advice for other young patient advocates was shared at the end of the session, followed by group discussions.
Basics of Electrocardiogram
CONTENTS
●Conduction System of the Heart
●What is ECG or EKG?
●ECG Leads
●Normal waves of ECG.
●Dimensions of ECG.
● Abnormalities of ECG
CONDUCTION SYSTEM OF THE HEART
ECG:
●ECG is a graphic record of the electrical activity of the heart.
●Electrical activity precedes the mechanical activity of the heart.
●Electrical activity has two phases:
Depolarization- contraction of muscle
Repolarization- relaxation of muscle
ECG Leads:
●6 Chest leads
●6 Limb leads
1. Bipolar Limb Leads:
Lead 1- Between right arm(-ve) and left arm(+ve)
Lead 2- Between right arm(-ve) and left leg(+ve)
Lead 3- Between left arm(-ve)
and left leg(+ve)
2. Augmented unipolar Limb Leads:
AvR- Right arm
AvL- Left arm
AvF- Left leg
3.Chest Leads:
V1 : Over 4th intercostal
space near right sternal margin
V2: Over 4th intercostal space near left sternal margin
V3:In between V2 and V4
V4:Over left 5th intercostal space on the mid
clavicular line
V5:Over left 5th intercostal space on the anterior
axillary line
V6:Over left 5th intercostal space on the mid
axillary line.
Normal ECG:
Waves of ECG:
P Wave
•P Wave is a positive wave and the first wave in ECG.
•It is also called as atrial complex.
Cause: Atrial depolarisation
Duration: 0.1 sec
QRS Complex:
•QRS’ complex is also called the initial ventricular complex.
•‘Q’ wave is a small negative wave. It is continued as the tall ‘R’ wave, which is a positive wave.
‘R’ wave is followed by a small negative wave, the ‘S’ wave.
Cause:Ventricular depolarization and atrial repolarization
Duration: 0.08- 0.10 sec
T Wave:
•‘T’ wave is the final ventricular complex and is a positive wave.
Cause:Ventricular repolarization Duration: 0.2 sec
Intervals and Segments of ECG:
P-R Interval:
•‘P-R’ interval is the interval
between the onset of ‘P’wave and onset of ‘Q’ wave.
•‘P-R’ interval cause atrial depolarization and conduction of impulses through AV node.
Duration:0.18 (0.12 to 0.2) sec
Q-T Interval:
•‘Q-T’ interval is the interval between the onset of ‘Q’
wave and the end of ‘T’ wave.
•‘Q-T’ interval indicates the ventricular depolarization
and ventricular repolarization,
i.e. it signifies the
electrical activity in ventricles.
Duration:0.4-0.42sec
S-T Segment:
•‘S-T’ segment is the time interval between the end of ‘S’ wave and the onset of ‘T’ wave.
Duration: 0.08 sec
R-R Interval:
•‘R-R’ interval is the time interval between two consecutive ‘R’ waves.
•It signifies the duration of one cardiac cycle.
Duration: 0.8 sec
Dimension of ECG:
How to find heart rhytm of the heart?
Regular rhytm:
Irregular rhytm:
More than or less than 4
How to find heart rate using ECG?
If heart Rhytm is Regular :
Heart rate =
300/No.of large b/w 2 QRS complex
= 300/4
=75 beats/mins
How to find heart rate using ECG?
If heart Rhytm is irregular:
Heart rate = 10×No.of QRS complex in 6 sec 5large box = 1sec
5×6=30
10×7 = 70 Beats/min
Abnormalities of ECG:
Cardiac Arrythmias:
1.Tachycardia
Heart Rate more than 100 beats/min
2024 Media Preferences of Older Adults: Consumer Survey and Marketing Implica...Media Logic
When it comes to creating marketing strategies that target older adults, it is crucial to have insight into their media habits and preferences. Understanding how older adults consume and use media is key to creating acquisition and retention strategies. We recently conducted our seventh annual survey to gain insight into the media preferences of older adults in 2024. Here are the survey responses and marketing implications that stood out to us.
Emotional and Behavioural Problems in Children - Counselling and Family Thera...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!
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The Importance of Black Women Understanding the Chemicals in Their Personal C...bkling
Certain chemicals, such as phthalates and parabens, can disrupt the body's hormones and have significant effects on health. According to data, hormone-related health issues such as uterine fibroids, infertility, early puberty and more aggressive forms of breast and endometrial cancers disproportionately affect Black women. Our guest speaker, Jasmine A. McDonald, PhD, an Assistant Professor in the Department of Epidemiology at Columbia University in New York City, discusses the scientific reasons why Black women should pay attention to specific chemicals in their personal care products, like hair care, and ways to minimize their exposure.
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Solution manual for managerial accounting 18th edition by ray garrison eric n...rightmanforbloodline
Solution manual for managerial accounting 18th edition by ray garrison eric noreen and peter brewer_compressed
Solution manual for managerial accounting 18th edition by ray garrison eric noreen and peter brewer_compressed
The Ultimate Guide in Setting Up Market Research System in Health-TechGokul Rangarajan
How to effectively start market research in the health tech industry by defining objectives, crafting problem statements, selecting methods, identifying data collection sources, and setting clear timelines. This guide covers all the preliminary steps needed to lay a strong foundation for your research.
"Market Research it too text-booky, I am in the market for a decade, I am living research book" this is what the founder I met on the event claimed, few of my colleagues rolled their eyes. Its true that one cannot over look the real life experience, but one cannot out beat structured gold mine of market research.
Many 0 to 1 startup founders often overlook market research, but this critical step can make or break a venture, especially in health tech.
But Why do they skip it?
Limited resources—time, money, and manpower—are common culprits.
"In fact, a survey by CB Insights found that 42% of startups fail due to no market need, which is like building a spaceship to Mars only to realise you forgot the fuel."
Sudharsan Srinivasan
Operational Partner Pitchworks VC Studio
Overconfidence in their product’s success leads founders to assume it will naturally find its market, especially in health tech where patient needs, entire system issues and regulatory requirements are as complex as trying to perform brain surgery with a butter knife. Additionally, the pressure to launch quickly and the belief in their own intuition further contribute to this oversight. Yet, thorough market research in health tech could be the key to transforming a startup's vision into a life-saving reality, instead of a medical mishap waiting to happen.
Example of Market Research working
Innovaccer, founded by Abhinav Shashank in 2014, focuses on improving healthcare delivery through data-driven insights and interoperability solutions. Before launching their platform, Innovaccer conducted extensive market research to understand the challenges faced by healthcare organizations and the potential for innovation in healthcare IT.
Identifying Pain Points: Innovaccer surveyed healthcare providers to understand their difficulties with data integration, care coordination, and patient engagement. They found widespread frustration with siloed systems and inefficient workflows.
Competitive Analysis: Analyzed competitors offering similar solutions in healthcare analytics and interoperability. Identified gaps in comprehensive data aggregation, real-time analytics, and actionable insights.
Regulatory Compliance: Ensured their platform complied with HIPAA and other healthcare data privacy regulations. This compliance was crucial to gaining trust from healthcare providers wary of data security issues.
Customer Validation: Conducted pilot programs with several healthcare organizations to validate the platform's effectiveness in improving care outcomes and operational efficiency. Gathered feedback to refine features and user interface.
Cyclothymia Test: Diagnosing, Symptoms, Treatment, and Impact | The Lifescien...The Lifesciences Magazine
The cyclothymia test is a pivotal tool in the diagnostic process. It helps clinicians assess the presence and severity of symptoms associated with cyclothymia.
The story of Dr. Ranjit Jagtap's daughters is more than a tale of inherited responsibility; it's a narrative of passion, innovation, and unwavering commitment to a cause greater than oneself. In Poulami and Aditi Jagtap, we see the beautiful continuum of a father's dream and the limitless potential of compassion-driven healthcare.
Cancer treatment has advanced significantly over the years, offering patients various options tailored to their specific type of cancer and stage of disease. Understanding the different types of cancer treatments can help patients make informed decisions about their care. In this ppt, we have listed most common forms of cancer treatment available today.
Test bank clinical nursing skills a concept based approach 4e pearson educati...rightmanforbloodline
Test bank clinical nursing skills a concept based approach 4e pearson education
Test bank clinical nursing skills a concept based approach 4e pearson education
Test bank clinical nursing skills a concept based approach 4e pearson education
4. What is
patient and
public
involvement?
When patients, carers and
members of the public are
active partners in research,
rather than the ‘subjects’ or
participants of research.
4
8. 8
In different
types of
research
Research in the
laboratory
Finding out what
needs to be researched
Testing new
treatmentsUnderstanding
experiences
Understanding
behaviours
14. What do I
bring to the
table?
14
My expertise and experience
My challenges and needs
My thoughts and ideas
My network
15. What I don’t
bring to the
table…
15
Your skills and experiences
Your needs and priorities
Your ‘tick’ in the box
The views of ‘all patients’
16. What impact
do I have on
research?
16
Improving research quality and relevance
Making information accessible
Making research acceptable and sensitive
Improving recruitment to research studies
Dispelling myths about research
Making research more relevant
17. What impact
can research
have on me?
Knowledge about my health
Skills to interpret evidence
Learning new techniques
Meeting incredible people
Improving self-confidence
26. 26
1. Patient and public involvement is no longer just
a ‘nice thing to do’.
2. Research is everyone’s business.
3. Patients, carers and members of the public must
be equal partners of research teams.
4. Start early, think about every possibility, review,
reflect and talk to each other.
5. Build relationships upon shared values of trust,
honesty and respect.
28. “
28
Just a big word… thrown
around by those who find it
easier to live in the world
they’ve been given, than to
explore the power they have
to change it.”
30. Useful
links
30
European League Against Rheumatism
https://www.eular.org
European Network for Children with Arthritis
https://www.enca.org
International Foundation for Autoimmune
and Autoinflammatory Arthritis (IFAA)
https://www.aiarthritis.org
National Institute for Health Research (NIHR)
https://www.nihr.ac.uk/patients-and-public/
http://www.invo.org.uk
Research Involvement and Engagement
https://researchinvolvement.biomedcentral.com
Versus Arthritis
https://www.versusarthritis.org