Defining and Evaluating the Impact of Physician Commitment to Nursing Home Practice: A Population‐Level Cross‐Sectional Study
Bibliographic record
Abstract
BACKGROUND: Medical care of complex nursing home (NH) residents in Canada is primarily managed by physicians. While physician commitment to NH practice is assumed to impact care quality, its influence on resident outcomes is inconsistent. This study quantifies commitment among NH physicians in Ontario, Canada, and its association with the quality of care among NH residents. METHODS: We conducted a retrospective cross-sectional study using multiple linked health administrative databases in 2022. We describe the practice patterns of the most responsible physician (MRP) of NH residents. We assessed three measures of commitment, including the proportion of NH practice based on residents, years in NH practice, and the number of NHs a physician worked in. Pearson-scaled Poisson and negative binomial regression models examined the relationship between commitment and resident outcomes, including medication prescriptions, emergency department (ED) visits, hospitalizations, and death. RESULTS: Our study identified 1368 NH MRPs practicing in 628 NHs and caring for 84,914 residents in Ontario. One hundred and fourteen (8.3%) had a ≥ 80% practice commitment to NH. The MRP cohort was generally male, had less than full-time practice, worked in more urban settings, and practiced in various settings beyond NH. We observed mixed associations between measures of commitment and resident outcomes, with some evidence suggesting that higher commitment could be beneficial. Residents receiving care from an MRP with ≥ 80% practice commitment had a reduced rate of ED visits (RR 0.90; 95% CI 0.83-0.99). CONCLUSIONS: Our work is the first to explore the impact of commitment in NH MRPs on resident care quality in Ontario, Canada. While commitment may be a factor, it is not the sole determinant of care quality. Further research is needed to refine how commitment is defined and measured and to consider additional factors beyond the physician, such as infrastructure, NH staff, and team collaboration, in how they influence care quality.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".