Resident perceptions of learning challenges in concussion care education
Bibliographic record
Abstract
Background: Resident-focused curricula that support competency acquisition in concussion care are currently lacking. We sought to fill this gap by developing and evaluating Spiral Integrated Curricula (SIC) using the cognitive constructivism paradigm and the Utilization-Focused Evaluation (UFE) framework. The evidence-based curricula consisted of academic half-days (AHDs) and clinics for first- and second-year family medicine residents. Our first pilot evaluation had quantitatively demonstrated effectiveness and acceptability but identified ongoing challenges. Here we aimed to better describe how concussion learning is experienced from the learners' perspective to understand why learning challenges occurred. Methods: A qualitative interpretative cohort study was utilized to explore resident perceptions of concussion learning challenges. Participants completed six monthly longitudinal case logs to reflect on their concussion exposure. Semi-structured interviews were conducted. Results: Residents' beliefs and perceptions of their roles influenced their learning organization and approaches. Challenges were related to knowledge gaps in both declarative knowledge and knowledge interconnections. Through reflection, residents identified their concussion competency acquisition gaps, leading to transformative learning. Conclusion: This Spiral Integrated Design created vigorous processes to interrogate "concussion" competency gaps. We discussed resident mindsets and factors that hindered "concussion" learning and potentially unintentional negative impacts on the continuity of patient care. Future studies could explore how to leverage humanistic adaptive expertise, cross-disciplines for curriculum development, and evaluation to overcome the hidden curriculum and to promote integrated education and patient care.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".