The CanMEDS Competency Framework in laboratory medicine: a phenomenographic study exploring how professional roles are applied outside the clinical environment
Bibliographic record
Abstract
Background: The CanMEDS Competency Framework is an internationally recognized model used to outline the proficiencies of a physician. It has predominantly been studied in clinical environments but not all medical specialties take part in direct patient contact. In laboratory medicine, the role of the physician is to promote and enhance patient diagnostics by managing and overseeing the functions of a diagnostic laboratory. Methods: This phenomenographic study explores the lived experiences of biochemistry, microbiology, and pathology residency program directors to better understand how they utilize the CanMEDS competencies. Eight laboratory medicine program directors from across Canada were individually interviewed using a semi-structured interview, and the data was analysed using inductive thematic analysis. Results: The findings show that the current framework is disconnected from the unique context of laboratory medicine with some competencies appearing unrelatable using the current standardized definitions and expectations. Nevertheless, participants considered the framework to be an appropriate blueprint of the competencies necessary for their professional environment, but to make it accessible more autonomy is required to adapt the framework to their needs. Conclusion: Newer renditions of the CanMEDS Competency Framework should better consider the realities of non-clinical disciplines.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".