“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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".