Motivations for Pursuing Enhanced Skill Credentials in Family Medicine: A Study of the Certificates of Added Competence in Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The College of Family of Physicians of Canada's Certificates of Added Competence (CACs) denote enhanced-skill family physicians who function beyond the scope of family practice or in specialized areas fundamental to family medicine practice. The credential provides recognition for skill development in areas of need and is intended to augment comprehensive care; however, there are concerns that it increases focused practice and decreases commitment to generalist care. To inform credentialing policies, we elucidated physician and trainee motivations for pursuing the CAC credential. METHODS: We conducted secondary analyses of interview data collected during a multiple case study of the impacts of the CACs in Canada. We collected data from six cases, sampled to reflect variability in geography, patient population, and practice arrangement. The 48 participants included CAC holders, enhanced-skill family physicians, generalist family physicians, residents, specialists, and administrative staff. We subjected data to qualitative descriptive analysis, beginning with inductive code generation, and concluding in unconstrained deduction. RESULTS: Family physicians and trainees pursue the credential to meet community health care needs, limit or promote diversity in practice, secure perceived professional benefits, and/or validate their sense of expertise. Notably, family physicians face barriers to engaging in enhanced skill training once their practice is established. CONCLUSIONS: While the CACs can enhance community-adaptive comprehensive care, they can also incentivize migration away from generalist practice. Credentialing policies should support enhanced skill designations that respond directly to pervasive community needs.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".