Predicting hospital transfers among nursing home residents in the last months of life
Bibliographic record
Abstract
BACKGROUND: Concerns have been raised over the practice of transferring nursing home residents to hospital at their end of life. OBJECTIVE: To examine the family and facility factors that may influence the decision to transfer nursing home residents to hospital in the last month of life. RESEARCH DESIGN: Secondary data analysis includes a sample of 119 bereaved family members from 21 nursing homes located in Central Canada. METHOD: A binary logistic regression analysis was conducted to explore the predictors for hospital transfers. RESULTS: Terminal hospital transfers were common: 70% of nursing home residents were sent to hospitals in the last month of their life, and the likelihood of terminal hospital transfers increased by having an adult child as decision-maker (odds ratio (OR) = 5.03; 95% confidence interval (CI) = 1.6, 16; significance level/probability value (p) = 0.007) or having a lower family income (OR = 2.9; 95% CI =1.1, 2.9; p = 0.027). Discussion and implications: The identified predictors for terminal hospital transfers are helpful in targeting and developing interventions to improve end-of-life care. Particular emphasis should therefore be placed on targeting families with low income and children of the nursing home residents for educational initiatives such as advance care planning awareness, in order to prevent terminal hospital transfers. It is hoped that policy-makers and practitioners can start addressing the findings of this study to reduce terminal hospital transfers at end of life and promote quality end-of-life care in nursing homes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".