Evidence-Based Practice in Healthcare

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Questions and Answers

In the context of evidence-based practice (EBP), delineate the nuanced interrelationship between 'clinical expertise,' 'best research evidence,' and 'patient values and preferences,' and elucidate how a discordance in one domain could potentially undermine the overall validity and applicability of a proposed intervention.

Clinical expertise informs the judicious application of evidence, while patient values contextualize the intervention's relevance. Discordance in any domain can lead to ineffective or undesirable outcomes, negating EBP's core tenets.

Critically evaluate the methodological differences between 'critically appraised individual articles', 'critically appraised topics', and 'systematic reviews' as high-level filters of evidence. How does each approach mitigate potential biases, and to what extent does the hierarchical structure accurately reflect the reliability and generalizability of findings?

Each minimizes bias through structured appraisal or synthesis. The hierarchy can falter if methodological rigor isn't consistently maintained within each level.

Given a complex clinical question requiring a comprehensive literature search, should the Boolean operators AND, OR, and NOT be applied sequentially or iteratively, and what specific weighting or prioritization should be assigned to each operator to optimize retrieval of relevant and high-quality evidence while minimizing the inclusion of irrelevant or confounding articles?

Apply iteratively, prioritizing AND to narrow concepts, then OR for synonyms, and NOT to exclude irrelevant terms. Weighting depends on the specificity of the search and the balance between sensitivity and precision.

Elaborate on the potential vulnerabilities inherent in employing truncation (*) and wildcards within database search strategies, particularly concerning the introduction of unintended semantic variations or the inadvertent inclusion of irrelevant results that may ultimately compromise the specificity and accuracy of the evidence synthesis process.

<p>Truncation can broaden the search too much, capturing irrelevant terms. Vigilance in reviewing results and refining the search is crucial to maintain specificity and accuracy.</p> Signup and view all the answers

Deconstruct the conceptual underpinnings and practical limitations of the CRAAP test (Currency, Relevance, Authority, Accuracy, Purpose) in evaluating the credibility and applicability of online health information resources, particularly when confronted with conflicting or ambiguous information from sources with varying degrees of transparency and expertise.

<p>CRAAP aids initial assessment but is subjective. Limitations include assessing authority definitively and detecting subtle biases, necessitating nuanced judgment based on source transparency and consistency with established evidence.</p> Signup and view all the answers

In epidemiological studies, how do prevalence and incidence rates provide complementary yet distinct insights into the dynamics of a disease, and what are the key assumptions and potential biases that must be considered when interpreting and comparing these measures across different populations or time periods?

<p>Prevalence captures existing cases, while incidence reflects new ones. Assumptions include consistent diagnosis and reporting. Biases arise from differences in population characteristics, detection methods, and disease duration.</p> Signup and view all the answers

Contrast the strengths and weaknesses inherent to various study designs—cross-sectional, case-control, cohort, and randomized controlled trials—regarding their capacity to establish causality; explain how each design addresses or fails to address key threats to internal validity, such as selection bias, confounding variables, and reverse causation.

<p>RCTs best address causality, while observational studies are more prone to bias. RCTs minimize confounding through randomization, while observational studies require careful adjustment for confounders.</p> Signup and view all the answers

Compare and contrast the methodological underpinnings of probability and non-probability sampling techniques, specifically regarding the generalizability of findings, the potential for selection bias, and the statistical assumptions required for valid inference; in what specific scenarios might non-probability sampling be ethically justifiable despite its inherent limitations?

<p>Probability sampling ensures representativeness but can be impractical. Non-probability sampling is cheaper but biased. It's justifiable when resources are limited and generalizability isn't the primary goal, or when studying rare populations.</p> Signup and view all the answers

What are the fundamental distinctions between allocation concealment and blinding in clinical trials, and how does each strategy independently and collectively contribute to the minimization of bias, enhancement of internal validity, and overall robustness of the study's conclusions, particularly in instances where subjective outcome measures are employed?

<p>Allocation concealment prevents selection bias during assignment, while blinding minimizes bias after assignment. Both enhance validity by reducing subjective influences on outcome assessment and interpretation.</p> Signup and view all the answers

Given a scenario where a statistically significant result is observed in a clinical trial, but the confidence interval around the effect estimate includes values that are not considered clinically meaningful, how should this discordance between statistical and clinical significance be interpreted, and what implications might it have for the translation of research findings into practical clinical recommendations or guidelines?

<p>Statistical significance doesn't guarantee clinical relevance. If the confidence interval includes clinically insignificant values, the effect might be too small to warrant changing practice, despite statistical significance.</p> Signup and view all the answers

Flashcards

Evidence-Based Practice

A systematic process that combines clinical expertise, best research evidence, and patient values to make informed decisions about patient care.

PICO

A framework used to structure and answer clinical questions. It includes Patient/Problem, Intervention, Comparison, and Outcome.

Boolean Operators

Techniques used to broaden or narrow search results when looking for evidence. AND narrows, OR broadens, NOT excludes.

Truncation

A search technique using a root word followed by '*' to find variations of the word.

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Prevalence

A measure of the number of existing cases of a disease in a population at a specific time.

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Incidence

A measure of the number of new cases of a disease that develop in a population over a specified time period.

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Cross Sectional Study

A potential problem with study design where a study is set up and the cause and outcome are measured at the same time.

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Recall Bias

A potential problem where participants' recall of past events is affected, leading to inaccurate data.

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Interviewer Bias

A potential problem where the interviewer's expectations influence the responses they receive.

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Outliers

A single value that is below are above the mean or average of the sample.

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Study Notes

  • Outlines the key concepts related to evidence-based practice, research methodologies, statistical analysis, and study designs.
  • It is a guide for understanding and applying research in healthcare.
  • Also includes ways to measure health and disease.
  • In addition the document includes ways to limit biases in studies

Evidence-Based Practice (EBP)

  • EBP is a combination of clinical expertise, the best research evidence, and patient values/preferences.
  • Process involves:
  • Assessing the patient
  • Asking a clinical question
  • Acquiring the best evidence
  • Appraising the evidence
  • Applying the evidence in conjunction with patient values.

PICO Framework

  • PICO is a framework used to formulate clinical questions:
    • Patient/Problem
    • Intervention (e.g., stopping Warfarin)
    • Comparison (e.g., continuing Warfarin)
    • Outcome

Quality of Evidence

  • Ranges from low (unfiltered information) to high (filtered):
    • Low: Background information, expert opinions, case-controlled studies, case series/reports, cohort studies.
    • High: Critically appraised articles/topics, systematic reviews.

Search Techniques

  • Boolean Operators:
    • AND combines different concepts to narrow results.
    • OR combines similar concepts/synonyms to broaden results.
    • NOT excludes irrelevant terms.
  • Truncation: Uses "" to search spelling variants of a root word (e.g., therap)
  • Brackets: Group terms with the same concept.
  • Example Search String: (Warfarin* OR Coumadin*) AND (dental extract* OR tooth extract* OR molar extract*)

CRAAP Test

  • Useful tool for evaluating sources.
    • Currency: Timeliness of the information.
    • Relevance: Importance of the information.
    • Authority: Source of information.
    • Accuracy: Reliability of the content.
    • Purpose: Reason the information exists.

Measuring Health and Disease (4W1 Goal)

  • Who: Person
  • What: Risk factors
  • Where: Place
  • When: Time
  • Goal is to intervene on risk factors/determinants

Disease Frequency

  • Prevalence: All current cases in a defined population within a defined period.
  • Incidence: New cases during the period being examined.

Data Types

  • Categorical (Nominal & Ordinal):
    • Nominal: No inherent Order
    • Ordinal: Ranked categories
  • Numerical (Continuous & Discrete):
    • Continuous: Any value within a range
    • Discrete: Countable & distinct

Study Designs

  • Cross-Sectional: Cause and outcome measured at the same time.
  • Case-Control: Starts with the outcome and looks for causes.
  • Cohort Studies: Starts with a cause and follows to observe outcomes over time.
  • Randomized Clinical Trial: Intervention is given, participants are randomly split into control/intervention groups.

Sampling Methods

  • Inclusion/Exclusion Criteria: Used to define the target population
  • Non-Probability Sampling:
    • Convenience/Judgment Sampling:
      • Easy, quick, but biased.
  • Simple Random Sampling:
    • Every member has an equal chance.
  • Systematic Sampling:
    • Fixed intervals from a random starting point
  • Stratified Sampling:
    • Divides population into subgroups and samples from each.
  • Cluster Sampling:
    • Divides population into clusters.

Randomization

  • Each individual has an equal chance of being assigned to each group.
  • Equalizes baseline differences between groups

Blinding

  • Prevents information bias by blinding participants to their study group.
  • Placebo Control:
    • Minimizes the placebo effect
      • Giving the control group a placebo

Measuring Treatment Effect

  • Absolute Risk Reduction: Incidence in control group minus incidence in the intervention group.
  • Relative Risk Reduction: (Incidence in control - Incidence in intervention) / Incidence in control.

Data Representation

  • Categorical Data: Uses bar charts and pie charts.
  • Continuous Data: Uses box plots and histograms.

Errors in Measurement

  • Systematic Error (Accuracy): Occurs consistently in the same direction.
  • Random Error (Precision): Varies randomly around the true value.

Bias

  • Systematic error in study design

Hypothesis Testing

  • Ho (Null Hypothesis): Assumes there is no difference between observations.
  • H1: Suggests there is a real difference between observations

Statistical Tests

  • Determines the type of outcome(numerical or categorical)
  • Can Determine the number of groups in the independent variable
  • Tests include; paired t-tests, Wilcoxon Signed Rank test etc

Confidence Interval

  • The "range" around a sample statistic.
  • Indicates the accuracy of the samples estimate.
  • A 95% confidence interval indicates how many times the study should be repeated to obtain the true value.

P-Value

  • Helps understand the probability of research results if the null hypothesis is true.
  • A small p-value (≤ 0.05) suggests sufficient evidence to reject the null hypothesis.

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