Assessing the Quality of Care Provided to Older Persons with Frailty in Five Canadian Provinces, Using Administrative Data
Bibliographic record
Abstract
Nous avons examiné la qualité des soins fournis aux personnes âgées fragiles dans cinq provinces canadiennes à partir de données administratives sur la santé. Dans chaque province, nous avons considéré les personnes âgées fragiles en fonction de deux cohortes : les personnes décédées et les personnes vivantes. Des règles de décision ont été utilisées pour déterminer quelles personnes étaient frêles, soit celles résidant en établissement de soins de longue durée, qui étaient en phase terminale ou dont le profil correspondait à deux des sept domaines identifiés. Ces domaines étaient fondés sur des échelles de fragilité, des discussions avec des gériatres et des indicateurs d'utilisation des services de santé. Nous avons évalué la qualité des soins à l'aide des indicateurs de qualité suivants : diminution de la durée de l'hospitalisation, diminution du nombre de réadmissions à l'hôpital, diminution du nombre de visites à l'urgence, augmentation de la continuité des soins fournis par un médecin de famille, diminution de l'utilisation de la ventilation mécanique et diminution du nombre d'admissions aux soins intensifs. À l'aide d'analyses de régression, nous avons également constaté que le sexe masculin et l'âge avancé étaient associés à une moins bonne qualité de soins dans les deux cohortes. Cette étude fournit des données de base qui permettront d'évaluer les futurs efforts visant à améliorer la qualité des soins offerts aux personnes âgées fragiles. We examined the quality of care provided to older persons with frailty in five Canadian provinces, using administrative health data. In each province, we identified two cohorts of older persons with frailty: decedents and living persons. Using decision rules, we considered individuals to be frail if they were long-term care residents, terminally ill, or met at least two of seven domains, which were based on frailty scales, geriatrician discussions, and health service utilization indicators. We assessed quality of care using selected quality indicators: decrease in length of hospital stay, decrease in the number of in-patient readmissions, decrease in the number of emergency department visits, increase in the level of family physician continuity of care, decrease in the use of mechanical ventilation, and decrease in the number of admissions to intensive care. Using regression analyses, we also found male sex and older age were associated with poorer quality of care in both cohorts. This study provides baseline data for evaluating future efforts to improve the quality of care provided to older persons with frailty.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".