Building Surgical Research Capacity Globally: Efficacy of a Clinical Research Course for Surgeons in Low-Resource Settings
Bibliographic record
Abstract
Musculoskeletal injury confers an enormous burden of preventable disability and mortality in low and moderate income countries. Appropriate orthopaedic and trauma care services are lacking. Leading international health agencies emphasize the critical need to create and sustain research capacity in the developing world as a strategic factor in the establishment of functional, independent health systems. One aspect of building research capacity is partnership between developing and developed countries, and knowledge sharing via these collaborations. This study evaluated the efficacy of a short, intensive course designed to educate surgeons on fundamental aspects of clinical research using evidence-based medicine principles. Orthopaedic surgeons from the United States and Canada presented a one-day course on the fundamentals of clinical research in Havana, Cuba. Knowledge acquisition was assessed on the part of course participants and surveyed current involvement with and attitudes towards clinical research. Questionnaires were presented to participants immediately preceding and following the course. The mean pre-test score was 43.9% (95% CI: 41.1-46.6%). The mean post-test score was 59.3% (95% CI: 56.5-62.1%). There were relative score increases in each subgroup based on professional level, subjective level of familiarity with evidence-based medicine concepts, and subjective level of experience in research. This study establishes the short-term efficacy of an intensive course designed to impart knowledge in evidence-based medicine and clinical research. Further study is necessary to determine the long-term benefits of this type of course. This may be a useful part of an overall strategy to build health research capacity in low and moderate-income countries, ultimately contributing to improved access to high-quality surgical 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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".