Family physicians providing regular care to residents in Ontario long-term care homes: characteristics and practice patterns.
Bibliographic record
Abstract
OBJECTIVE: To describe the characteristics and practice patterns of family physicians who regularly treat long-term care (LTC) residents in order to inform quality improvement strategies. DESIGN: Cross-sectional study involving a 2005 province-wide census of LTC residents' charts linked to additional health care administrative databases. SETTING: All LTC homes in Ontario. PARTICIPANTS: Residents aged 66 years and older (n = 50375) and the family physicians (n = 1190) most responsible for their care. MAIN OUTCOME MEASURES: Distribution of LTC residents across family physicians, and physician demographic characteristics and practice patterns. RESULTS: The distribution of residents across physicians was highly skewed (median 27 residents, mean 42.5 residents). The care of 90.4% of residents was accounted for by 628 (52.8%) identified physicians. Family physicians practising in LTC facilities were more likely to be older (mean age 52.4 years vs 48.2 years, P < .001) and male (82.4% vs 61.5%, P < .001) than other family physicians. Urban physicians who provided care to LTC residents had bigger LTC practices than rural LTC physicians did (median 50 residents vs median 12 residents). CONCLUSION: About 600 family physicians are responsible for the regular care of more than 90% of LTC residents in Ontario and quality improvement efforts could be aimed at this relatively small group of physicians. Half of the urban physicians who practise in LTC homes are responsible for 50 or more LTC residents. This might represent a key part of their overall practice.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".