Digital health competences and AI beliefs as conditions for the practice of evidence-based medicine: a study of prospective physicians in Canada
Bibliographic record
Abstract
Background The practice of evidence-based medicine (EBM) has become pivotal in enhancing medical care and patient outcomes. With the diffusion of innovation in healthcare organizations, EBM can be expected to depend on medical professionals’ competences with digital health (dHealth) and artificial intelligence (AI) technologies.Objective We aim to investigate the effect of dHealth competences and perceptions of AI on the adoption of EBM among prospective physicians. By focusing on dHealth and AI technologies, the study seeks to inform the redesign of medical curricula to better prepare students for the demands of evidence-based medical practice.Methods A cross-sectional survey was administered online to students at the University of Montreal’s medical school, which has approximately 1,400 enrolled students. The survey included questions on students’ dHealth competences, perceptions of AI, and their practice of EBM. Using structural equation modeling (SEM), we analyzed data from 177 respondents to test our research model.Results Our analysis indicates that medical students possess foundational knowledge competences of dHealth technologies and perceive AI to play an important role in the future of medicine. Yet, their experiential competences with dHealth technologies are limited. Our findings reveal that experiential dHealth competences are significantly related to the practice of EBM (β = 0.42, p < 0.001), as well as students’ perceptions of the role of AI in the future of medicine (β = 0.39, p < 0.001), which, in turn, also affect EBM (β = 0.19, p < 0.05).Conclusions The study underscores the necessity of enhancing students’ competences related to dHealth and considering their perceptions of the role of AI in the medical profession. In particular, the low levels of experiential dHealth competences highlight a promising starting point for training future physicians while simultaneously strengthening their practice of EBM. Accordingly, we suggest revising medical curricula to focus on providing students with practical experiences with dHealth and AI technologies.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".