Does spaced education improve clinical knowledge among Family Medicine residents? A cluster randomized controlled trial
Bibliographic record
Abstract
Spaced education is a learning strategy to improve knowledge acquisition and retention. To date, no robust evidence exists to support the utility of spaced education in the Family Medicine residency. We aimed to test whether alerts to encourage spaced education can improve clinical knowledge as measured by scores on the Canadian Family Medicine certification examination. METHOD: We conducted a cluster randomized controlled trial to empirically and pragmatically test spaced education using two versions of the Family Medicine Study Guide mobile app. 12 residency training programs in Canada agreed to participate. At six intervention sites, we consented 335 of the 654 (51%) eligible residents. Residents in the intervention group were sent alerts through the app to encourage the answering of questions linked to clinical cases. At six control sites, 299 of 586 (51%) residents consented. Residents in the control group received the same app but with no alerts. Incidence rates of case completion between trial arms were compared using repeated measures analysis. We linked residents in both trial arms to their knowledge scores on the certification examination of the College of Family Physicians of Canada. RESULTS: Over 67 weeks, there was no statistically significant difference in the completion of clinical cases by participants. The difference in mean exam scores and the associated confidence interval did not exceed the pre-defined limit of 4 percentage points. CONCLUSION: Further research is recommended before deploying spaced educational interventions in the Family Medicine residency to improve knowledge.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".