Residency spiral concussion curriculum design
Bibliographic record
Abstract
BACKGROUND: Resident-focused concussion curricula that measure learner behaviours are currently unavailable. We sought to fill this gap by developing and iteratively implementing a Spiral Integrated Concussion Curriculum (SICC). APPROACH: Programme elements of the concussion curriculum include academic half-days (AHDs) and three half-day clinics for first- and second-year family medicine residents. Our SICC utilises social cognitive learning principles, the constructivism paradigm and utilisation-focused evaluation. EVALUATION: A mixed-method evaluation with a pre-/post-test design and interviews was utilised. Surveys and knowledge tests were used to measure knowledge and confidence pre-AHD and 6 months post-AHD. Interviews at 6 months explored programme perception and behaviour change. Of the 141 programme attendees, 114 (80%) participated in the pre-intervention knowledge test and 33 completed the pre- and post-AHD test. Immediate pre-/post-testing demonstrated statistically significant improvement in knowledge (p = 0.042). At 6 months post-AHD, residents in Cycle 1 (n = 5) had a knowledge decrease of 3.33% (p > 0.05). Residents in Cycle 2 (n = 7) had a knowledge increase of 11.6% (p > 0.05). Both cycles of residents had an increase in confidence (Cycle 1: 65.0% [p = 0.025]; Cycle 2: 62.8% [p = 0.0014]). Residents (5 out of 6) reported positive behavioural changes at 6 months. Valued programme elements included concussion diagnosis and management, the self-study guide resource and the organised structure. IMPLICATIONS: The SICC enriched these residents' learning and fostered sustained knowledge improvement and behavioural change at 6 months post-intervention. This approach may provide a workable design for future competency-based curriculum development.
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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 | 0.006 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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; both teacher heads agree on what is shown here.
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".