“Nightmares–Family Medicine” Course Is an Effective Acute Care Teaching Tool for Family Medicine Residents
Bibliographic record
Abstract
INTRODUCTION: Simulation is an effective method for teaching acute care skills but has not been comprehensively evaluated with family medicine (FM) residents. We developed a comprehensive simulation-based approach for teaching acute care skills to FM residents and assessed it for effectiveness. METHOD: We compared the effectiveness of our standard acute care simulation training [Acute Care Rounds (ACR)] to a more comprehensive simulation-based acute care program, Nightmares-Family Medicine (NM). We used a self-reported comfort scale as well as video-captured performance on an acute care Objective Structured Clinical Examination (OSCE). Seventy-seven of our FM residents in their postgraduate year 1 between July 2012 and June 2015 participated in the study. Wilcoxon matched pairs and one-tailed t tests analysis was used for analyzing the comfort scale, Whitney-Mann, and χ for the OSCE performance. RESULTS: Nightmares-Family Medicine's initial 2-day session significantly improved the resident's self-assessment scores on all 20 items of the questionnaire (P < 0.05). Time-matched ACR improved 11 of 20 items (P < 0.05) level. Follow-up NM sessions improved 5 to 8 of 20 items (P < 0.05). Follow-up ACR sessions improved 1 to 5 of 20 items (P < 0.05). The means taken at the end of postgraduate year 1 year were higher for 13 of 20 items in the NM group (P < 0.05) as compared with ACR group. The NM group scored significantly higher on both the mean scores of OSCE individual categories (P < 0.01) and the Global Assessment Score (P < 0.05). Significantly less NM residents failed the OSCE (n = 1/30, 3.3% vs n = 8/37, 21.6%, P < 0.05). CONCLUSIONS: "Nightmares-Family Medicine" course is very effective at teaching acute care skills to FM residents and more so than our previous curriculum.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".