Development of a novel conceptual framework for curriculum design in Canadian postgraduate trauma training
Bibliographic record
Abstract
BACKGROUND: Recent changes in practice patterns and training paradigms in trauma care have resulted in a critical review of postgraduate curricula. Specifically, a shift towards non-operative management of traumatic injuries, and reduced resident work-hours, has led to a significant decrease in trainees' surgical exposure to trauma. The purpose of our study is to perform an exploratory review and needs assessment of trauma curricula for general surgery residents in Canada. METHODS: Our study design includes semi-structured interviews with trauma education experts across Canada and focus groups with various stakeholder groups. We performed qualitative analysis of comments, with two independent reviewers, using inductive thematic analysis to identify themes and sub-themes. RESULTS: We interviewed four trauma education experts and conducted four focus groups. We formulated two main themes: institutional context and transferability of curricular components. We further broke down institutional context into sub-themes of culture, resources, trauma system, and trauma volume. We developed a new conceptual framework to guide ongoing curricular reform for trauma care within the context of general surgery training. CONCLUSIONS: The proposed framework, developed through qualitative analysis, can be utilized in a collaborative fashion in the curricular reform process of trauma care training in Canada.
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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 | 0.047 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.018 | 0.038 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".