Instructional methods used by health sciences librarians to teach evidence-based practice (EBP): a systematic review
Bibliographic record
Abstract
Background: Librarians often teach evidence-based practice (EBP) within health sciences curricula. It is not known what teaching methods are most effective.Methods: A systematic review of the literature was conducted searching CINAHL, EMBASE, ERIC, LISTA, PubMed, Scopus, and others. Searches were completed through December 2014. No limits were applied. Hand searching of Medical Library Association annual meeting abstracts from 2009–2014 was also completed. Studies must be about EBP instruction by a librarian within undergraduate or graduate health sciences curricula and include skills assessment. Studies with no assessment, letters and comments, and veterinary education studies were excluded. Data extraction and critical appraisal were performed to determine the risk of bias of each study.Results: Twenty-seven studies were included for analysis. Studies occurred in the United States (20), Canada (3), the United Kingdom (1), and Italy (1), with 22 in medicine and 5 in allied health. Teaching methods included lecture (20), small group or one-on-one instruction (16), computer lab practice (15), and online learning (6). Assessments were quizzes or tests, pretests and posttests, peer review, search strategy evaluations, clinical scenario assignments, or a hybrid. Due to large variability across studies, meta-analysis was not conducted.Discussion: Findings were weakly significant for positive change in search performance for most studies. Only one study compared teaching methods, and no one teaching method proved more effective. Future studies could conduct multisite interventions using randomized or quasi-randomized controlled trial study design and standardized assessment tools to measure outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".