Primary Care Continuity and Location of Death for Those with Cancer
Bibliographic record
Abstract
BACKGROUND: Continuity of primary care is known to be associated with both improved processes and outcomes of care. Despite continuity being a desired attribute of end-of-life care and despite the desire by most patients with cancer to die at home, there has been no health services research examining this relationship. AIM: To examine the association between family physician continuity of care and the location of death for patients with cancer. DESIGN OF STUDY: A retrospective population-based study involving secondary data analysis of four linked administrative health databases spanning 6 years of information (1992-1997). SETTING: Nova Scotia, Canada Participants: All those who died of cancer from 1992 to 1997 and had made at least three ambulatory visits to a family physician. METHODS: The relationship of provider continuity of care and an out-of-hospital death was examined using logistic regression. RESULTS: Out-of-hospital deaths accounted for 31.6% of the 9714 deaths in the study population. The mean provider continuity of care was 0.78 (standard deviation [SD] 0.22). Those who died out-of-hospital had a greater odds of having received high provider continuity (adjusted odds ratio [OR] = 1.54, 95% confidence interval [CI] = 1.22, 1.93) when compared to those who died in-hospital. There appears to be a modification of this effect by gender with a significant association found for males and not for females. The trends in the point estimates are, however, similar for both sexes. CONCLUSIONS: This study demonstrates an association between family physician continuity of care and the location of death for those with advanced cancer. Such continuity should be fostered in the development of models of integrated service delivery for end-of-life care.
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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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".