An innovative approach to identifying learning needs for intrinsic CanMEDS roles in continuing professional development
Bibliographic record
Abstract
CONTEXT: The CanMEDS framework promotes the development of competencies required to be an effective physician. However, it is still not well understood how to apply such frameworks to CPD contexts, particularly with respect to intrinsic competencies. OBJECTIVE: This study explores whether physician narratives around challenging cases would provide information regarding learning needs that could help guide the development of CPD activities for intrinsic CanMEDS competencies. METHODS: We surveyed medical and surgical specialists from Southern Ontario using an online survey. To assess perceived needs, participants were asked, 'Describe three CPD topic you would like to learn about in the next 12 months'. To identify learning needs that may have arisen from problems encountered in practice, participants were asked, 'Describe three challenging situations encountered in the past 12 months.' Responses to the two open-ended questions were analyzed using thematic content analysis. RESULTS: Responses were received from 411 physicians, resulting in 226 intrinsic CanMEDS codes for perceived learning needs and 210 intrinsic codes for challenges encountered in practices. Discrepancies in the frequency of intrinsic roles were observed between the two questions. Specifically, Leader (28%), Scholar (43%), and Professional (16%) roles were frequently described perceived learning needs, as opposed to challenges in practice (Leader: 3%; Scholar: 2%; and Professional: 8%. Conversely, Communicator 39%, Health Advocate 39%, and to a lesser extent Collaborator 11%) roles were frequently described in narratives surrounding challenges in practice, but appeared in <10% of descriptions of perceived learning needs (Communicator: 4%; Health Advocate 6%; Collaborator: 3%). CONCLUSION: The present study provides insight into potential learning needs associated with intrinsic CanMEDS competencies. Discrepancies in the frequency of intrinsic CanMEDS roles coded for perceived learning needs and challenges encountered in practice may provide insight into the selection and design of CPD activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".