Module 1 – Notes PDF

Module 1 – Notes 2025-01-19 1:27 PM Research – Module 1: What is Evidence-Informed Practice (EIP)? It’s a TRIAD 1. Best Available Research Evidence...

Module 1 – Notes 2025-01-19 1:27 PM Research – Module 1: What is Evidence-Informed Practice (EIP)? It’s a TRIAD 1. Best Available Research Evidence 2. Professional/Clinical Expertise 3. Client Values & Individualized Needs Core Principles The better the research evidence, the more confident our clinical decisions Evidence alone is never sufficient to make clinical decisions Why Does This Matter to RMT's? By following evidence-informed/evidence-based standards of care, RMTs and their treatments will be: 1. More effective 2. Better supported by extended benefits insurance plans 3. More standardized among members of the profession 4. More reputable members of the health care community Quantitative Vs. Qualitative Methods Both have limitations, but both are useful parts of scientific research Both asses the credibility of a study's reliability and validity Depends on the question being asked, some studies use both methods Qualitative analysis often helps develop a specific and quantifiable hypothesis Quantitative statistics can help illuminate patterns in qualitative studies However, most of health care research utilizes quantitative methods 1. Quantitative Methods Are based on the assumption that there is a uniform reality that can be observed, measured, and expressed in numbers Also assumes linear cause and effect (Eg. A specific treatment causes a specific outcome) Test hypotheses and use numbers to summarize information Researchers manipulate the treatment setting and participants - They control as much of environment as possible Major focus on ruling out "rival explanations" for outcomes - that is, accounting for confounding variables - then use stats to show probability of chance - if there is a low probability of chance, outcomes should be reproducable 2. Qualitative Methods Assume that any observation effects what is being observed and vise versa No assumption of a single reality - any description is one of several realities that may be valid No assumption of linear cause and effect - multiple factors/causes may influence an outcome Importance is placed on observing in the natural setting (there is no controlled setting) Observer is part of the process Data is collected via interviews, direct observation, and documents (such as journals, correspondence, and questionnaires/surverys) Descriptive Vs Explanatory Studies Descriptive Studies Data used to describe a group/sample/population, with no intention of going beyond that sect These studies do not test a theory, nor are they used to learn more about the sect. These studies set the stage to eventually test a theory by first forming one Descriptive studies FORM a hypothesis Weaker evidence due to lack of control or comparison groups, but still explore cause and effect relationships - however, contribute to the weight of evidence when combined w/ consistent results from observation & experimental studies Foundation for hypothesis based on observations - provide detailed info that helps to refine the design of explanatory studies Examples: case studies, case series, correlation studies, qualitative studies Explanatory Studies Studies that seek to make generalized statements about a population based on a studied sample These studies look to see if, for example, a population benefits from an intervention Aka inferential studies Explanatory studies TEST a hypothesis Stronger evidence that clarifies or establishes cause and effect Provide evidence about research questions (eg, disease prevalence or treatment efficacy) Examples are divided in observational and experimental - Observational: cross-sectional, case-control, cohort studies - Experimental: before and after treatment studies, clinical trials Types of Studies: Cohort Study OBSERVATIONAL EXPLANATORY STUDY Prospective, longitudinal, observational studies Attempt to explain relationship b/w treatment and outcome Prospective = outcome has not yet occurred Cohort = a group who all experience the same treatment (or other variable); or exhibit the same characteristics (eg. Risk factor for CVD) Members of the cohort are observed over a long period of time to see what the outcome is – eg. Some develop CVD, others don’t etc. Pros: provide strong observational "evidence" of a relationship b/w treatment/risk factors and the outcome Cons: take a long time and are expensive Attrition is often high (eg. Participants drop out, die, move out of the region, etc.) Before/After without Control EXPIRIMENTAL EXPLANATORY STUDY A type of case series, but rather than just observing an outcome, practitioner determines hypothesis; sets eligibility criteria and methods; collects provides tx; measures outcome for a series of patients (making it an experimental case series) Often performed by practitioners in their own practices Risk of many weaknesses Lacks a control group for comparison; potentially over-estimates the treatment effect Data collection may be subjective and patients may over-report good outcomes (possibly b/c they already have a relationship with the practitioner) May not be possible to generalize the findings to anyone outside the test group Before/After with Control Same, but less limitation d/t use to control, making it a stronger study Case Report DESCRIPTIVE STUDY Describes events related to the care of a single patient Better than anecdote, d/t thorough rationale with presentation, description, detail, and discussion along with directions for future investigation Pros: can serve as the basis for a new hypothesis; but can be used to report adverse reactions to treatment Case Series DESCRIPTIVE STUDY Takes the case study a step further by combining individual case studies of similar patients Pros: may be the first indication of a new phenomena Anecdote A brief, revealing account of an individual person or an incident Not evidence, as there cause does not equal effect; lacks rationale, detail, and exploration Can be used to create a case report/case series Correlation Study DESCRIPTIVE STUDY Population survey using existing data about groups NOT evidence, as correlation does not equal causation To does not prove cause and effect. It describes an association b/w exposure and outcome Pros: quick way to see if there's an association/correlation between an exposure and an outcome Pros: relatively low cost study; simple and quick to conduct Components of a Research Article: Abstract Summary including background, purpose, design, methods, results, conclusion, and discussion Introduction Thorough description of the purpose/importance, states the research question, and includes a literature review (which places a study in context) States the purpose for conducting the research Introduces the research question which the study addresses (sometimes the hypothesis) Methods Detailed description of how the study was carried out Readers should be able to decide if other explanations exist that explain the findings, or if the authors conclusions are strong Method should be so exact that the study is replicable by other researchers Results/Findings Description of the analysis of the study data Can be qualitative or quantitative Objective; neither supports nor dismisses the hypothesis or research question Conclusion/Discussion Answers the question "so what do these results mean in terms of the research question?" Authors interpret the research results As the discussion sometimes puts forward the authors informed opinions, its good when they cite other studies that point to similar results References Listing of other research, articles, etc that the authors consulted while preparing the article youre reading References should provide a good source of further reading Hypothesis: Statement that can be demonstrated to be true or false through the methodical gathering and analysis of empirical information or data An educated guess on how things work Should be testable and measurable Will have a: 1. Independent variable – things to be changed 2. Dependent variable – thing to be measured The null hypothesis is the commonly accepted fact; it is the opposite of the alternate hypothesis Researchers work to reject, nullify or disprove the null hypothesis by coming up with an alternate hypothesis that they believe explains a phenomenon and, therefore, rejects the null Technically, researches do not prove a hypothesis, but disprove a null Null Hypothesis The null hypothesis assumes there is no relationship between two variables and that controlling one variable has no effect on the other Examples Cats show no preference for food based on shape Plant growth is not affected by light colour Age has no effect on musical ability Variables: Independent Variable Cause Influencer Manipulating Example – the liquid used to water each plant Dependent Variable Effect What is being influenced Measuring – any measurable changes *depends* on the independent variable Example – the height or health of the plant Confounding Variable Any variable other than the independent variable that influences the dependent variable Can/should be controlled or tested itself in a high quality study Controlled Variable Everything you want to remain constant and unchanging Example – type of plant used, pot size, amount of liquid, soil type etc. Type of Experiment Designs: By number of independent variables Simple (1 independent variable) vs complex (>1 independent variable) By subject assignment Between-subjects designs (independent designs): different subjects are used in each group Within-subjects designs (repeated measures designs): the same subjects are used in each group Mixed designs: include both between and within-subject components Between-subjects designs Each participant participates in one and only one condition of the experiment Within-subjects designs All participants participate in all of the conditions of the experiments