How Often, Where, and by Which Specialty Do Long-Term Care Home Residents Receive Specialist Physician Care? A Retrospective Cohort Study
Bibliographic record
Abstract
This retrospective cohort study describes the rates, location, and determinants of specialist physician visits among 257,216 long-term care (LTC) residents across 648 LTC homes in Ontario, Canada, between 2007 and 2016. Visit rates in the last year of life were calculated for a sub-cohort of residents who died in LTC between 2013 and 2016. Visits were measured per resident-year using physician billings. Over 10 years, the rate of visits to specialists outside the LTC home was consistently higher than within LTC (2.99 vs. 1.55 visits/resident-year). Residents were less likely to receive specialist care if they were older, had dementia, or lived in urban LTC homes. From 12 months before death to the last week of life, rates of specialist visits increased by 246% and 56% inside and outside of LTC, respectively. Improving access to physician specialist care in LTC homes may reduce burdensome transitions and improve resident quality of life.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".