Clinical factors associated with recent medical care visits in nursing homes: a multi-site cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: We examined which resident-level clinical factors influence the provision of a recent medical care visit in nursing homes (NHs). DESIGN: Multi-site cross-sectional. SETTING AND PARTICIPANTS: We extracted data on 3,556 NH residents from 18 NH facilities in Ontario, Canada, who received at minimum, an admission and first-quarterly assessment with the Resident Assessment Instrument Minimum Data Set (MDS) 2.0 between November 1, 2009, and October 31, 2017. METHODS: We conducted a secondary analysis of routinely collected MDS 2.0 data. The provision of a recent medical care visit by a physician (or authorized clinician) was assessed in the 14-day period preceding a resident's first-quarterly MDS 2.0 assessment. We utilized best-subset multivariable logistic regression to model the adjusted associations between resident-level clinical factors and a recent medical care visit. RESULTS: Two thousand eight hundred fifty nine (80.4%) NH residents had one or more medical care visits prior to their first-quarterly MDS 2.0 assessment. Six clinically relevant factors were identified to be associated with recent medical care visits in the final model: exhibiting wandering behaviours (OR = 1.34, 95% CI 1.09 - 1.63), presence of a pressure ulcer (OR = 1.37, 95% CI 1.05 - 1.78), a urinary tract infection (UTI) (OR = 1.52, 95% CI 1.06 - 2.18), end-stage disease (OR = 9.70, 95% CI 1.32 - 71.02), new medication use (OR = 1.31, 95% CI 1.09 - 1.57), and analgesic use (OR = 1.24, 95% CI 1.03 - 1.49). CONCLUSIONS AND IMPLICATIONS: Our findings suggest that resident-level clinical factors drive the provision of medical care visits following NH admission. Clinical factors associated with medical care visits align with the minimum competencies expected of physicians in NH practice, including managing safety risks, infections, medications, and death. Ensuring that NH physicians have opportunities to acquire and strengthen these competencies may be transformative to meet the ongoing needs of NH residents.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".