A Moderated Journal Club Is More Effective than an Internet Journal Club in Teaching Critical Appraisal Skills: Results of a Multicenter Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Evidence Based Reviews in Surgery (EBRS) is an Internet journal club that is effective in teaching critical appraisal skills to practicing surgeons. The objective of this randomized controlled trial was to determine whether teaching critical appraisal skills to surgical residents through the Internet is as effective as a moderated in-person journal club. STUDY DESIGN: Twelve general surgery programs were cluster-randomized to an Internet group (6 programs; 227 residents; 23 to 47 residents/program) or a moderated journal club (6 programs, 216 residents, 21 to 72 residents/program). Each EBRS package includes a clinical and methodological article plus clinical and methodological reviews. Residents in the Internet group were required to complete 8 EBRS packages online plus participate in an online discussion group. Residents in the moderated group were required to attend 8 journal clubs moderated by a faculty member. All residents completed a validated test assessing expertise in critical appraisal. RESULTS: In the Internet group, only 18% of residents completed at least 1 EBRS package compared with 96% in the moderated group. One hundred and thirty (57.8%) residents in the Internet group completed the test compared with 157 (72.7%) in the moderated group. The residents in the moderated group scored considerably better on the critical appraisal test, with a mean score of 42.1 compared with 37.4 in the Internet group (p = 0.05), with a moderate effect size of 0.6 SD. CONCLUSIONS: A moderated journal club is considerably better in teaching critical appraisal skills to surgical residents. This is likely because of the low participation in the Internet journal club.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".