Family Physicians with Certificates of Added Competence in Palliative Care Contribute to Comprehensive Care in Their Communities: A Qualitative Descriptive Study
Bibliographic record
Abstract
Background: Since 2015, the College of Family Physicians of Canada has certified enhanced skills in palliative care (PC) with a certificate of added competence. Aim: This study aimed to describe the ways family physicians with enhanced skills in PC contribute within their communities, the factors that influence ways of practicing, and the perceived impacts. Design: Secondary analysis of data from a multiple case study on the role and impacts of family physicians with enhanced skills (i.e., PC physicians) was undertaken. Setting/Participants: Interviews were conducted in 2018 to 2019 with PC and generalist family physicians and residents associated with six family medicine practice cases across Canada. An unconstrained qualitative content analysis was performed. Results: Twenty-one participants (nine PC physicians, five generalist family physicians, two residents, and five physicians with enhanced skills in other domains) contributed data. PC physicians worked by enhancing their own family practice or as focused PC physicians. Roles included collaborating with other physicians through consultations, comanaging patients (shared care), or assuming care of the patient as the main provider (takeover). PC physicians increased capacity among their colleagues, with some patient care and education activities not being remunerated. Funding models and other structures were perceived as incentivizing the takeover model. Conclusion: Family physicians with enhanced skills in PC contribute to comprehensive care through the end of life. Remuneration should support system capacity and relationships that enable family physicians to provide primary PC especially outside the takeover model.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".