The Role of Family Physicians in Cancer Care: Perspectives of Primary and Specialty Care Providers
Bibliographic record
Abstract
BACKGROUND: Currently, the specific role of family physicians (fps) in the care of people with cancer is not well defined. Our goal was to explore physician perspectives and contextual factors related to the coordination of cancer care and the role of fps. METHODS: Using a constructivist grounded theory approach, we conducted telephone interviews with 58 primary and cancer specialist health care providers from across Canada. RESULTS: The participants-21 fps, 15 surgeons, 12 medical oncologists, 6 radiation oncologists, and 4 general practitioners in oncology-were asked to describe both the role that fps currently play and the role that, in their opinion, fps should play in the future care of cancer patients across the cancer continuum. Participants identified 3 key roles: coordinating cancer care, managing comorbidities, and providing psychosocial care to patients and their families. However, fps and specialists discussed many challenges that prevent fps from fully performing those roles: ■ The fps described communication problems resulting from not being kept "in the loop" because they weren't copied on patient reports and also the lack of clearly defined roles for all the various health care providers involved in providing care to cancer patients.■ The specialists expressed concerns about a lack of patient access to fp care, leaving specialists to fill the care gaps. The fps and specialists both recommended additional training and education for fps in survivorship care, cancer screening, genetic testing, and new cancer treatments. CONCLUSIONS: Better communication, more collaboration, and further education are needed to enhance the role of fps in the care of cancer patients.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".