Family physicians and cancer care. Palliative care patients' perspectives.
Bibliographic record
Abstract
OBJECTIVE: To explore factors that affect the integrity of palliative cancer patients' relationships with family physicians and to ascertain their perceptions of their FPs' roles in their care. DESIGN: Qualitative study using grounded-theory methods, taped semistructured interviews, and chart reviews. SETTING: Two palliative care hospital wards in Winnipeg, Man. PARTICIPANTS: A purposeful sample of 11 men and 14 women. METHOD: Qualitative content analysis of interview transcripts. MAIN FINDINGS: Cancer care is organized in a sequential, parallel, or shared manner between FPs and cancer specialists, with sequential care a common outcome if patients' relationships with their FPs wane. Cancer patients can lose contact with FPs because of patient or physician relocation, distrust over delays in diagnosis, failure to perceive a need for FPs, poor communication between FPs and specialists, and a lack of FP involvement in the hospital. People with cancer value FPs for being accessible through prompt appointments and telephone contact; for providing emotional and family support; and for referral, triage, and general medical care. CONCLUSION: Family physicians can enhance care of cancer patients. Contact with FPs can be maintained by ensuring good communication between specialists and FPs, defining a clear role for FPs, addressing concerns about delays in diagnosis, and referring patients back to FPs, particularly after hospitalization.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".