Learning Activity Sequence, Simulation, and Productive Failure in Anti-Harassment Education
Bibliographic record
Abstract
INTRODUCTION: Despite the high prevalence of harassment in health professions education, empirical investigations into effective anti-harassment interventions remain scarce. Our study examined the effectiveness of an innovative anti-harassment intervention that featured instructional videos and high-fidelity simulation training for medical residents. We drew from the knowledge-learning-instruction framework and the idea of productive failure to conceptualize our research questions and discussion. We examined how the sequence of educational activities may be linked to simulation performance and increases in knowledge levels. METHOD: Our pretest posttest study contacted all 88 eligible internal medicine residents, randomly assigning them to the intervention (n = 60) or equivalent training later (n = 28). Of those assigned to the intervention, 52 (86.6%) consented and were further randomized to a video-first or simulation-first group. We provided educational activities in different sequences accordingly. We assessed knowledge via a questionnaire, and simulation performance via a performance checklist. RESULTS: The video-first group demonstrated significantly better initial simulation performance compared with the simulation-first group, aligning with traditional learning sequential benefits. Both groups showed significant postintervention knowledge gains. Notably, the simulation-first group, despite starting with lower performance, ended with significantly higher knowledge levels than the video-first group, suggesting the efficacy of the productive failure approach in fostering deeper learning. CONCLUSIONS: The sequence of educational activities may impact the effectiveness of anti-harassment training. The video-first approach enhances immediate performance, while the simulation-first approach fosters better knowledge retention. Further research should investigate the long-term effects of such educational strategies and their applicability in diverse healthcare settings.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".