A General Needs Assessment for Postgraduate Canadian Trauma Training in General Surgery
Bibliographic record
Abstract
Background:\nTrauma is considered a key component of surgical training. However, recent changes in practice patterns and training paradigms have resulted in a critical review of curricula for surgical residents. Specifically, a shift towards non-operative management of traumatic injuries, improvements in motor vehicle safety, and reduced resident work-hours have led to a critical decrease in surgical exposure to trauma. The purpose of this study is to perform a general needs assessment of trauma curricula for general surgery residents across Canada.\nStudy Design:\nThe study design consisted of three parts: (1) a detailed review of existing curricula across Canada; (2) semi-structured interviews with trauma education experts from various sites across the country; and (3) focus groups with varied stakeholder groups. Participants were selected using purposive sampling. Interview guides were designed using two related curricular conceptual frameworks; Kern’s systems-based approach to curriculum development and Lee's four-dimensional curriculum framework for the health professions. The initial interview tool was piloted and initial data led to modifications in successive iterations. Qualitative analysis was performed using inductive thematic analysis by two independent reviewers in order to identify key themes and subthemes.\nResults:\nFour trauma education experts participated in the semi-structured interviews and four separate focus groups with varied stakeholders were conducted. Through inductive thematic analysis, two main themes were identified: (1) institutional context and (2) transferability of curricular components. Institutional context was further broken down into sub-themes of culture, resources, trauma system, and trauma volume. Transferability was applied to the broad categories of trainee outcomes and education strategies. A new conceptual framework was developed to guide ongoing curricular reform for trauma within the context of general surgery training.\nConclusion:\nThis general needs assessment for trauma training in Canada has provided valuable data to guide a national curriculum development process. We believe that the framework presented here is also generalizable to other settings using appropriate contextual lenses.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".