Barriers and Facilitators to Self-Directed Learning in Continuing Professional Development for Physicians in Canada: A Scoping Review
Bibliographic record
Abstract
PURPOSE: This scoping review explored the barriers and facilitators that influence engagement in and implementation of self-directed learning (SDL) in continuing professional development (CPD) for physicians in Canada. METHOD: This review followed the six-stage scoping review framework of Arksey and O'Malley and of Daudt et al. In 2015, the authors searched eight online databases for English-language Canadian articles published January 2005-December 2015. To chart and analyze data from the 17 included studies, they employed a two-step analysis process composed of conventional content analysis followed by directed coding applying the Theoretical Domains Framework (TDF). RESULTS: Conventional content analysis generated five categories of barriers and facilitators: individual, program, technological, environmental, and workplace/organizational. Directed coding guided by the TDF allowed analysis of barriers and facilitators to behavior change according to two key groups: physicians engaging in SDL, and SDL developers designing and implementing SDL programs. Of the 318 total barriers and facilitators coded, 290 (91.2%) were coded for physicians and 28 (8.8%) for SDL developers. The majority (209; 65.7%) were coded in four key TDF domains: environmental context and resources, social influences, beliefs about consequences, and behavioral regulation. CONCLUSIONS: This scoping review identified five categories of barriers and facilitators in the literature and four key TDF domains where most factors related to behavior change of physicians and SDL developers regarding SDL programs in CPD were coded. There was a significant gap in the literature about factors that may contribute to SDL developers' capacity to design and implement SDL programs in CPD.
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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 | 0.049 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.032 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".