Characteristics and practice patterns of family physicians who provide home visits in Ontario, Canada: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Physician home visits are essential for populations who cannot easily access office-based primary care. The objective of this study was to describe the characteristics, practice patterns and physician-level patient characteristics of Ontario physicians who provide home visits. METHODS: This was a retrospective cross-sectional study, based on health administrative data, of Ontario physicians who provided home visits and their patients, between Jan. 1, 2019, and Dec. 31, 2019. We selected family physicians who had at least 1 home visit in 2019. Physician demographic characteristics, practice patterns and aggregated patient characteristics were compared between high-volume home visit physicians (the top 5%) and low-volume home visit physicians (bottom 95%). RESULTS: = 6242), the top 5% were more likely to be male and practise in large urban areas, and rarely saw patients who were enrolled to them (median 4% v. 87.5%, standardized mean difference 1.12). High-volume physicians' home visit patients were younger, had greater levels of health care resource utilization, resided in lower-income and large urban neighbourhoods, and were less likely to have a medical home. INTERPRETATION: A small subset of home visit physicians provided a large proportion of home visits in Ontario. These home visits may be addressing a gap in access to primary care for certain patients, but could be contributing to lower continuity of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".