Which primary Care Physicians Deliver Home Visits to Their Dying Patients in Ontario? A Retrospective Cohort Study
Bibliographic record
Abstract
Context: Home visits have become increasingly uncommon although evidence suggests they improve healthcare quality and reduce overall expenditures. Objective: This study identifies the number of physicians delivering home visits at patients’ end of life, describes characteristics of primary care physicians delivering end-of-life home visits, and explores associations with delivery. Study Design and Analysis: A retrospective cohort design with descriptive analysis of association between primary care physician characteristics and the propensity to deliver home visits to patients at the end of life. Setting or Dataset: Ontario, Canada using population-level health administrative data housed at ICES. Population Studied: Primary care physicians in Ontario, Canada between April 1, 2014-March 31, 2019, who were registered in the College of Physicians and Surgeons of Ontario database (CPSO) dataset on or after January 1, 1990 and as of March 31, 2016. Intervention/Instrument: Patients who were in their last year of life. Outcome Measures: Home visits delivered Results: A total of 9,884 physicians were identified, of which 2,568 (25.7%) delivered at least one end-of- life home visit. Physician characteristics showing increased odds ratio (OR) of home visit delivery were older age (OR 1.01 [95% Confidence Interval (CI): 1.00-1.02]) international training (OR 1.28 [95% CI:1.04-1.59]), previous home visit experience (OR 1.02 [95% CI: 1.01-1.02]), capitation models of remuneration; namely enhanced fee-for-service models (OR 1.5 [95%CI: 1.17-2.00]) and mainly capitation model (OR 1.4 [95% CI:1.11-1.79]), and population size of practice location with highest odds in small rural or remote areas (<9,000 residents) (OR 1.38 [95%CI: 1.02-1.88]) and the largest metropolitan areas (OR 1.84 [95%CI: 1.46-2.57]). Conclusions: This research demonstrates primary care physicians’ characteristics influence home visit practice patterns. Furthermore, it highlights characteristics amenable to policy or system-level changes that could increase the provision of home visits. Increasing physician home services could greatly improve the dying experience of Canadians.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".