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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".