Principles and Practice of Clinical Research course for surgeons: an evaluation of knowledge transfer and perceptions
Bibliographic record
Abstract
BACKGROUND: Knowledge and training in evidence-based medicine (EBM) and clinical research is under-represented in most surgical training programs in North America. To address a lack of resources for surgeons, trainees and related specialties, we developed a Principles and Practice of Clinical Research (PPCR) course. The current study evaluated transfer of knowledge and perceptions about the course. METHODS: The course was an intensive 2.5-day workshop consisting of interactive lectures and small group breakout sessions. Pre- and postcourse tests were completed by participants. The Fresno test, questions from the Centre of Applied Medical Statistics (CAMS) test and questions developed by the course chairs were used to determine if participants' knowledge of EBM, clinical research methodology and statistics improved. We also elicited participant perceptions of the course. RESULTS: Overall participant knowledge about EBM and clinical research methods improved significantly from the pre- to the postcourse test (mean improvement inscore 13.5%, relative increase 35.3%, p < 0.001). Specifically, improvements were demonstrated on the Fresno test (mean improvement 13.5%, relative increase 36.1%, p< 0.001) and the CAMS test (mean improvement 11.4%, relative increase 20.1%, p = 0.001). Participants showed the greatest improvement in general knowledge about clinical research (mean improvement 15.4%, relative increase 46.5%, p < 0.001). The PPCR course was well received; 30 (81.1%) participants who completed the course evaluation gave it a positive rating. CONCLUSION: Participants in a short course focusing on EBM and clinical research methodology had significant improvements in scores on tests of knowledge gained. Widespread implementation of similar courses may bridge knowledge gaps for surgeons, surgical trainees and health professionals. Whether shorter knowledge gain sare retained in the longer term remains unknown.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".