Preparing future healthcare professionals for evidence-based decision-making using health technology assessment as an experiential learning tool
Bibliographic record
Abstract
The rapid evolution of healthcare, driven by the rise of new health technologies and the growing emphasis on evidence-based care, requires health professionals skilled in assessing and utilizing evidence to support decision-making. Health technology assessments (HTAs), which involve evidence synthesis and appraisal, analysis across various domains, and tailoring evidence to local contexts, can develop these skills. These skills are also part of medical competencies. However, there is a dearth of literature on teaching HTAs to health professionals and on how the skills learned through HTA align with medical competencies. We developed an innovative experiential project that had students conduct mini-HTAs for community partners considering new technologies. Students worked in teams to evaluate and synthesize evidence, delivering a website and brief to our partners justifying their recommendations. Success was achieved by providing authentic tasks and using supportive learning strategies. The project engaged students deeply, offering them valuable skills for post-graduation work and education. In this article, we demonstrate how the learning outcomes of the project align with the CANMEDS framework of medical competencies. Secondly, we demonstrate an effective, adaptable, and unique approach of using mini-HTAs to equip students with the knowledge and skills needed to navigate the rapidly evolving landscape of evidence and technologies, preparing them as our future healthcare professionals.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".