Evaluating the Association between the Implementation of the PoET Southwest Spread Project and Reductions in Acute Care Transfers from Long-Term Care: A Quasi-Experimental Matched Cohort Study Using Population-Level Health Administrative Data
Bibliographic record
Abstract
OBJECTIVES: To measure changes in resident-level acute care transfer rates after the PoET Southwest Spread Project (PSSP), and to identify patient and long-term care (LTC) home characteristics associated with acute care transfers after program launch. DESIGN: Quasi-experimental matched (1:1 ratio) cohort study design using linked population-based health administrative data. SETTING: Sixty publicly funded LTC homes (PSSP = 30; control = 30) in Ontario, Canada, from November 2019 to December 2021. METHODS: We matched 30 PSSP homes to 30 control homes with similar characteristics and described incidence rates for resident-level acute care transfers during the 7-month post-implementation period. We used generalized linear mixed models to evaluate the association between PSSP implementation and acute care transfers during the post-implementation period. We adjusted resident-level characteristics (ie, age, sex, comorbidity status) and home-level characteristics (ie, rurality status, profit model, COVID-19 impact). We identified a decedent sub-cohort to measure transfer patterns during the last 2 months of life. RESULTS: A matched cohort of 8894 residents (PSSP = 4103; control = 4791) was captured. Incidence rates of transfers increased during the post-implementation period for both PSSP (78.8 to 80.9 transfers per 1000 person-months) and control residents (66.9 to 67.9 transfers per 1000 person-months). After adjusting for covariates of interest, PSSP exposure was associated with a reduction in acute care transfers during the post-implementation period after adjusting for covariates (incidence rate ratio, 0.73; 95% CI, 0.62-0.87; P = .0002). Older age and select health regions were associated with reduced transfers, whereas higher comorbidity status and higher COVID-19 outbreak days were associated with increases. Similar patterns persisted for transfers during the last 2 months of life. CONCLUSIONS AND IMPLICATIONS: This study systematically evaluated the impact of an ethics-based health care intervention in LTC using health care utilization databases. PoET implementation is associated with reduced acute care transfer rates, especially in the last 2 months of life in LTC.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".