Resident-Level Factors Associated with Hospitalization Rates for Newly Admitted Long-Term Care Residents in Canada: A Retrospective Cohort Study
Bibliographic record
Abstract
Chez les résidents en soins de longue durée (SLD), l'hospitalisation peut amener des complications telles que le déclin fonctionnel. L'objectif de notre étude était d'examiner l'association entre les données démographiques et de santé et le taux d'hospitalisation des résidents nouvellement admis en SLD. Nous avons mené une étude de cohorte rétrospective incluant tous les centres de SLD de six provinces et d'un territoire du Canada, à l'aide des données de la RAI-MDS 2.0 et de la Discharge Abstract Database. Nous avons inclus les résidents nouvellement admis ayant eu une évaluation entre le 1er janvier et le 31 décembre 2013 (n = 37 998). Les résidents de sexe masculin avec une santé plus instable et une déficience fonctionnelle de modérée à grave présentaient des taux d'hospitalisation plus élevés, tandis que les résidents avec une déficience cognitive de modérée à grave avaient des taux moindres. Les résultats de notre étude pourraient contribuer à l'identification des résidents nouvellement admis qui seraient plus à risque d'hospitalisation et à l'élaboration de stratégies préventives plus ciblées, incluant la réadaptation, la planification préalable de soins, les soins palliatifs et les services gériatriques spécialisés. Hospitalizations of long-term care (LTC) residents can result in adverse outcomes such as functional decline. The objective of our study was to investigate the association between demographic and health information and hospitalization rate for newly admitted LTC residents. We conducted a retrospective cohort study of all LTC homes in six provinces and one territory in Canada, using data from the Resident Assessment Instrument–Minimum Data Set (RAI-MDS) 2.0 and the Discharge Abstract Database. We included newly admitted residents with an assessment between January 1 and December 31, 2013 (n = 37,998). Residents who were male, had higher health instability, and had moderate or severe functional impairment had higher rates of hospitalization, whereas residents who had moderate or severe cognitive impairment had decreased rates. The results of our study can be used to identify newly admitted residents who may be at risk for hospitalization, and appropriately target preventative interventions, including rehabilitation, advance care planning, palliative care, and geriatric specialty services.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".