EBP Workshops & Seminars

Participants can choose one of two EBP streams (Teaching or Practicing)

 

1. Teaching Objectives​

 

An overarching objective of the workshop is to renew and enhance participants’ enthusiasm about teaching EBP, and to kindle their imagination about ways to improve their teaching. After participation in the Evidence-Based Practice workshop, the learner will develop the knowledge base and/or skill set to:

 

• Describe approaches for effective teaching of evidence-based clinical practice in different educational settings (on the wards, in out-patient clinics, tutorials, journal clubs, and large group sessions).

• Understand and describe the process of incorporating evidence-based care into clinical practice.

• Advance their evidence-based clinical practice skills

• Describe effective and interactive means of teaching in small and large groups settings.

• Identify high-quality evidence-based resources for the purpose of patient care, teaching, and continuing education through independent learning

 

​2. Practicing Objectives

 

An overarching objective of the workshop is to renew and enhance participants’ enthusiasm about learning EBP, and to kindle their imagination about ways to improve their learning and incorporation of the concepts into clinical practice. After participation in the Evidence-Based Practice workshop, the learner will develop the knowledge base and/or skill set to:

 

• Advance their evidence-based clinical practice skills

• Understand and describe the process of incorporating Evidence-Based Practice into clinical practice

• Apply evidence-based clinical practice skills to real-world clinical problems

• Demonstrate ability to solve clinically oriented problems in an interactive problem-based small group learning format

• Identify high-quality evidence-based resources for the purpose of patient care and continuing education through independent learning

​

Examples content/EBP concepts covered in workshops:

 

1. Systematic error vs random error

2. Study designs (experimental versus observational)

 

Therapy or prevention 

1. Evaluating risk of bias 

a. Sampling and selection bias

b. Randomization 

c. Allocation concealment 

d. Prognostic balance 

e. Blinding 

f. Completeness of follow-up and missing outcome data

g. Intention-to-treat analysis 

h. Per-protocol analysis

i. Complete case analysis

j. As treated analysis

k. Stopping trials early 

 

2. Interpreting study results

i. Measures of effect 

a. 2x2 tables 

b. Relative Risk 

c. Relative Risk Reduction 

d. Absolute Risk Reduction 

e. Number Needed to Treat (benefit and harm)

f. Odds Ratios

g. Mean Difference

ii. Statistical significance

iii. Precision of estimate of treatment effect 

a. Confidence intervals

b. Number of events or observations

iii. Magnitude of treatment effect

a. Minimal Important Difference (MID)

b. Decision thresholds

 

3. Applying the results to patient care or policy

a. Study patients’/environment vs. clinical patient/environment 

b. Balance of all important outcomes addressed (e.g., patient-reported outcomes) 

c. Trading off desirable vs. undesirable health outcomes 

d. Sufficient duration of follow-up 

e. Interventional harms and costs 

f. Values, preferences, beliefs and attitudes in decision-making

 

Summarizing the evidence 

a. Review types

i. Narrative review 

ii. Systematic review 

iii. Systematic review and meta-analysis 

iv. Systematic review and network meta-analysis

v. Systematic review and individual patient data meta-analysis

b. Summary estimate or pooled estimate of effect 

i. Relative Risk 

ii. Odds Ratios

iii. Hazard Ratios

iv. Absolute Difference

v. Mean Difference

vi. Standardized Mean Difference units

vii. Ratio of Means

viii. Minimal Important Difference (MID) units

c. Forest Plots 

i. weighting 

ii. fixed and random effect models including DerSimonian-Laird, Peto, Hartung-Knapp models; frequentist vs Bayesian

d. Heterogeneity (visual inspection, statistical inspection including I2 and p-value and determining credibility of observed subgroup effects)

e. Reporting bias (e.g., outcome reporting and publication bias, funnel plots) 

f. Moving from evidence to recommendations

i. Certainty of evidence (e.g., Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) 

ii. Evidence to decision frameworks (e.g., magnitude of effect, certainty of evidence, and values and preferences, as well as cost considerations, equity, acceptability, and feasibility)

​

​