Does Primary Care Model Effect Healthcare at the End of Life? A Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Comprehensive primary care may enhance patient experience at end of life. OBJECTIVE: To examine whether belonging to different models of primary care is associated with end-of-life healthcare use and outcomes. DESIGN: Retrospective population cohort study, using health administrative databases to describe health services and costs in the last six months of life across three primary care models: enrolled to a physician remunerated mainly by capitation, with incentives for comprehensive care and access in some to allied health practitioners (Capitation); remunerated mainly from fee-for-service (FFS) with smaller incentives for comprehensive care (Enhanced FFS); and not enrolled, seeing physicians remunerated solely through FFS (Traditional FFS). SETTING: People who died from April 1, 2010 to March 31, 2013 in Ontario, Canada. MEASURES: Health service utilization, costs, and place of death. RESULTS: Approximately two-thirds (62.7%) of decedents had more contact with a specialist than family physician. Those in Capitation models were more likely to have the majority of physician services provided by a family physician (44.9% vs. 38.6% in Enhanced FFS and 34.3% in Traditional FFS) and received more home care service days (mean 27.2 vs. 24.2 in Enhanced FFS and 21.7 in Traditional FFS). And 22.5% had a home visit by a family physician. Controlling for potential confounders, decedents spent significantly more days in an institution in Enhanced FFS (1.1, 95% confidence interval [CI]: 0.9-1.5) and Traditional FFS (2.2, 95% CI: 1.8-2.6) than in Capitation. CONCLUSION: Decedents in comprehensive primary care models received more care in the community and spent less time in institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".