Research Methods Questions

• A 5-step model of Evidence-Based Practice provides insight into the overall process.
• Step 1: Pinpoint the information needed and formulate a targeted, searchable clinical question.
• Step 2: Search for the highest quality research evidence to address the question.
• Step 3: Perform a critical evaluation of the research evidence based on its applicability and quality.
• Step 4: Integrate the rigorously assessed research evidence with clinical expertise and patient values and circumstances, V&C.
• Step 5: Assess the efficacy and effectiveness of steps 1-4, and determine methods for future improvements.
• Evidence is supported by three cornerstones: optimizing patient outcomes, integrating clinical expertise, scientific research, and patient values and circumstances.
• Three source of evidence include: clinical expertise, scientific research, and patient values and circumstances.
• The evidence pyramid illustrates a hierarchy, with systematic reviews at the top.
• The evidence pyramid indicates systematic reviews as most likely to be high-quality, followed by randomized clinical trials, cohort studies, case-control studies, case series, case studies, and narrative reviews, expert opinions, and textbooks as least likely to be high-quality.
• Six elements comprise patient management: examination, evaluation, diagnosis, prognosis, intervention, and outcomes measurement.
• Examination involves gathering patient data via history-taking, systems review, and tests and measures; may also identify need for referral.
• Evaluation includes making clinical judgments based on collected data.
• Diagnosis categorizes data into clusters or syndromes to inform prognosis and intervention strategies.
• Prognosis determines the care plan (optimal improvements and interventions) and timeline to achieve the desired level.
• Intervention includes shared decision-making, reassessment, and necessary adjustments.
• Outcomes measurement includes results of patient management and the impact of PT interventions.
• Background questions seek general information and lack specificity to an individual, typically used when unfamiliar with the patient and usually answered with textbooks and reliable web pages
• Foreground questions relate to a specific patient, condition, and clinical outcome of interest, typically answered through research studies.
• PICO components form a searchable clinical question about interventions.
• P stands for Patient (or population) and their clinical characteristics.
• I stands for Intervention.
• C stands for Comparison.
• O stands for Outcomes.
• Clinical Practice Guidelines, CPGs, are systematically developed statements with recommendations based on the best available research evidence.
• Using question-relevant CPGs published within 5 years and scoring high on specific criteria helps to avoid reading multiple individual studies.
• CPGs offer expertise to address research gaps.
• Boolean operators assist you with combining search terms.
• "AND" narrows search results to articles containing all specified terms.
• "OR" broadens search results to include articles containing any specified terms.
• Twelve questions assist in determining the applicability of research, questions 1 through 5, and quality, questions 6 through 10
• Applicability is determined by its pertinence to the clinical question.
• Start by reading the abstract to pinpoint relevance
• Consider whether the study population is relevant to the patient and the patient's characteristics, such as age and background.
• Assess if the intervention and comparison/control groups receive clinically realistic interventions in enough detail in the method section, feasibility
• Determine whether the outcome measures relate to the clinical question, were conducted realistically, provide information to use the results, demonstrate patient benefit, and align with the ICF model.
• Check if the duration follow-up is sufficient to assess the intervention's impact on patients' lives and the time needed for them to resume work or daily activities.
• Was there randomized assignment to intervention groups?
• Randomized Controlled Trials/Randomized Clinical Trials: The gold standard, considered one of the most valid designs, reduce bias
• Does the recruitment strategy minimize bias and use specific procedure?
• Consecutive sampling includes subjects referred with an issue.
• Selective sampling includes ideal subjects and is less rigorous but common and practical
• Were all participants accounted for, including a comparison to the original grouping?
• Details about dropouts and reasons inform appraisal.
• Was blinding/masking optimized in the study design such as evaluators, participants and therapists)?
• Should group treatment result in a research study which measures the outcomes?
• Aside from the allocated treatment, were groups treated equally, requiring all aspects of treatments to be equivalent?
• Are funding sources and author conflicts known?
• Transparency about possible conflicts impacts the ability of the reader to use it. Applicability is the evaluation and determination of people who could benefit.
• In research, notation means:
• R means subjects are randomized or not.
• X means intervention.
• O means observation/time of measurement.
• "Construct" refers to what is intend to be measured.
• "Measure" refers to how construct is measured
• "Score" refers to measures based on the measure
• Screening measures estimate the probability of a healthy person developing a certain condition in the future, e.g., cardiovascular risk.
• Diagnostic measures determine the presence or absence of a condition, e.g., Lachman test for ACL rupture.
• Prognostic tools predict whether or when a patient will recover, e.g., Örebro Musculoskeletal Pain Questionnaire.
• Treatment-based classification tools guide the direction of patient management, e.g., Start Back Tool.
• Outcome measures track the level of a symptom, function, or disease marker, e.g., Patient-Specific Functional Scale.
• Subjective measures rely on personal judgment.
• Objective measures involve observable and measurable factors.
• Research suggests reliability and validity are independant of measure.
• Recognize constructs subjetivity like a patient's pain.
• Distribution of data can be assessed with:
• Normal distributions rely on measurements of people that can be graphed (Bell-shaped curve)
• Skewed distributions are common in clinical populations due to disease/injury.
• Descriptive statistics provide an overview of typical values and variability within and between groups.
• The mean is the average, derived from a sum of observations that estimates the variability
• Standard deviation measures deviation.
• The median represents distribution.
• Confidence intervals provide mean values.
• Range represents distribution by difference between values
• Data types include:
• Nominal (categorical) data lacks order such as dichotomous scales for 2 categories.
• Ordinal data involves ranking but imprecise scales like the Likert or Rankin scales.
• Continuous (ratio) data ranks with intervals.
• Interval data precisely measures properties like ratio scales.
• Reliability refers to:
• Intra-individual measures that remain uniform across measures.
• Intra-rater repeatability by therapists within a group.
• Inter-rater repeatability between therapists in a group.
• Reliability is quantified through the interclass correlation coefficient, ICC, to ascertain reliability.
• Hypothesis testing includes:
• Type 1 errors that falsely conclude that there is a statistically significant difference when there isn't one actually.
• Type 2 errors that falsely conclude that there is not a statistically significant difference when there is one actually. Inferential statistics rely on data to reach informed conclusions such as:
• Effect size through Cohen's d, to determine mean distributions and how distinct they are across two groups.
• P-values which determine chance that it contributed.
• Number needed to treat, NNT, to determine rates of outcomes in tested groups.
• Relative Reduction Risk, RRR, indicates these could indicate the interventional tool succeeded.
• Absolute Reduction Risk, ARR, indicates the the intervention group should be preferred
• Change in the score on a measure at or before baseline is measured on the:
• Indivudal and Mean
• Difference is calculated with data from groups. The between-group is the mean score on an measure that called, "Treatmen Effect"
• Treatment effects compare if a patient's treatment improves B outcome and are measured scores between groups.
• Treatment response is viewed for effectiveness in light of B intervention.
• Responders have a larger outcome but can respond to Treatment B.
• Many factors, not one single factor, can affect/improve clinical trials:
• Effects of treatment.
• Expectations of patients.
• Progression of condition.
• Measurement.
• Statisical significance requires consideration if summary are equal within the model.
• Clinical significance requires large group for the measure, allowing consideration of the difference for group.
• P value Represents the probability that the the difference between the wean of 2 groups is as big or bigger, if melt and from the assumptions
• Description of the group involves findings to the effects and meanings.
• Size of effect takes group size into consideration.
• Representative Samples take population into consideration, with the only missing factor between group
• Samples include the popouplation Cronbach's alpha measures correlations. consistency of between the
• Primary differences between a Sample and a Pop.
• population, Includes all patients wla Inferential statistics are is meant to some study.
• Null-hypothesis testing is a group
• Reliability is measured/established:
• Internal consistency assesses multiple items measuring the same construct.
• Cronbach's alpha ranges 0-1 and shows score correlation.
• Test-retest establishes extent to which the measure produces some result.
• Validity measures the abilities to measures for features its intending measure
• Validity measures:
• Content validity Establishes the characteristic with the outcome
• Face validity Based that the appears measyres to the feature and measyres it right
• How compares
• compares to features
• measure and the features
• Construct the measures
• Known with known for the the features
• Covergent that relate to the the feature
• Disriminant the features that
• Clinically Important
• Stusy powered and designs will help comparitive help modatilies and patients

Questions covering research transparency, applicability, notation, measurement terminology (constructs, measures, scores), and validity assessment specifically concurrent and predictive design. It also covers the use of screening measures and standards involved in validity assessment.