Backward Planning a Craniomaxillofacial Trauma Curriculum for the Surgical Workforce in Low‐Resource Settings
Bibliographic record
Abstract
BACKGROUND: Trauma is a significant contributor to global disease, and low-income countries disproportionately shoulder this burden. Education and training are critical components in the effort to address the surgical workforce shortage. Educators can tailor training to a diverse background of health professionals in low-resource settings using competency-based curricula. We present a process for the development of a competency-based curriculum for low-resource settings in the context of craniomaxillofacial (CMF) trauma education. METHODS: CMF trauma surgeons representing 7 low-, middle-, and high-income countries conducted a standardized educational curriculum development program. Patient problems related to facial injuries were identified and ranked from highest to lowest morbidity. Higher morbidity problems were categorized into 4 modules with agreed upon competencies. Methods of delivery (lectures, case discussions, and practical exercises) were selected to optimize learning of each competency. RESULTS: A facial injuries educational curriculum (1.5 days event) was tailored to health professionals with diverse training backgrounds who care for CMF trauma patients in low-resource settings. A backward planned, competency-based curriculum was organized into four modules titled: acute (emergent), eye (periorbital injuries and sight preserving measures), mouth (dental injuries and fracture care), and soft tissue injury treatments. Four courses have been completed with pre- and post-course assessments completed. CONCLUSIONS: Surgeons and educators from a diverse geographic background found the backward planning curriculum development method effective in creating a competency-based facial injuries (trauma) course for health professionals in low-resource settings, where contextual aspects of shortages of surgical capacity, equipment, and emergency transportation must be considered.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".