Evaluating the Association between the Implementation of the PoET (Prevention of Error-Based Transfers) Southwest Spread Project and Palliative Care Provision: A Quasi-Experimental Matched Cohort Study Using Population-Level Health Administrative Data
Bibliographic record
Abstract
OBJECTIVES: The PoET (Prevention of Error-based Transfers) project seeks to align long-term care (LTC) home informed consent practices to existing legislation, thereby reducing consent-related error-based transfers to acute care. We sought to measure changes in resident-level palliative care provision after participating in the PoET Southwest Spread Project (PSSP), and to identify patient and LTC home characteristics associated with palliative care provision. DESIGN: Quasi-experimental matched (1:1 ratio) cohort study design using linked population-based health administrative data. SETTING: Sixty LTC homes (PSSP = 30; Control = 30) in Ontario, Canada, from November 2019 to December 2021. METHODS: We matched 30 PSSP to 30 control homes and described incidence rates for resident-level palliative care provision (ie, physician palliative care encounters and palliative medication prescriptions) during the 7-month postimplementation period. We used generalized linear mixed models to evaluate the association between PSSP implementation and palliative care provision during the postimplementation period. We adjusted for resident-level characteristics (ie, age, sex, comorbidity status) and home-level characteristics (ie, rurality status, profit model, COVID-19 impact). We identified a decedent subcohort to measure palliative care provision patterns during the last 2 months of life. RESULTS: We captured a matched cohort of 8894 residents (PSSP = 4103; Control = 4791). Incidence rates of palliative care encounters increased during the postimplementation period for PSSP (82.6 to 85.4 per 100 person-months) but not for control residents (68.8 to 65.3 per 100 person-months). After adjusting for key covariates, PSSP exposure was associated increased palliative care provision (incidence rate ratio 2.47, 95% CI 2.31-2.64) and palliative care medication prescription (1.16, 95% CI 1.12-1.20). Larger home size, certain health regions, and higher number of comorbidities were associated with increased physician palliative care encounters. CONCLUSIONS AND IMPLICATIONS: By promoting correct informed consent practices in LTC, PSSP participation increased palliative care provision for PSSP LTC residents across all settings.
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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 | 0.009 | 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 | 0.002 | 0.002 |
| 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".