Assessing Physician Motivation to Engage in Continuing Professional Development on Artificial Intelligence
Bibliographic record
Abstract
ABSTRACT: To realize the transformative potential of artificial intelligence (AI) in health care, physicians must learn how to use AI-based tools effectively, safely, and equitably. Continuing professional development (CPD) activities are one way to learn how to do this. The purpose of this article is to describe a theory-based approach for assessing health professionals' motivation to participate in CPD on AI-based tools. An online survey, based on an AI competency framework developed from existing literature and expert consultations, was administered to practicing physicians in Ontario, Canada. Across eight subcompetencies for using AI-based tools (eg, appraise AI-based tools for their regulatory and legal status), the survey measured physicians' perception they could successfully enact the competency, the importance of the competency in meeting their practice needs, and the desirability of participating in CPD activities on the competency. Motivation scores were calculated by multiplying the three scores together. Ninety-five physicians completed the survey. The highest motivation scores were for the subcompetency of identifying AI-based tools based on clinical needs, while the lowest motivation scores were for appraising tools' regulatory and legal status. All AI subcompetencies were generally rated as important, and CPD activities were generally perceived as desirable. This survey demonstrates the utility of a theory-based approach for assessing physicians' motivation to learn. Although the survey results are context specific, the approach may be useful for other CPD providers to support decision making about future AI-related CPD activities.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".