Determinants of place of Death for recipients of Home-Based Palliative Care
Bibliographic record
Abstract
INTRODUCTION: Health system restructuring combined with the preferences of many terminally ill care recipients and their caregivers has led to an increase in home-based palliative care, yet many care recipients die within institutional settings such as hospitals. This study sought to determine the place of death and its predictors among palliative care patients with cancer. METHODS: Study participants were recruited from the Temmy Latner Centre for Palliative Care, a regional palliative care program based in Toronto, Canada. A total of 137 patients and their family caregivers participated in the study; application of various exclusion criteria restricted analysis to a sub-sample of 110. Bivariate (chi-square) and multivariate (logistic regression) analyses were conducted. RESULTS: 66 percent of participants died at home. Chi-square analysis indicated that women were more likely to die at home than men; multivariate analysis indicated that women and those living with others were significantly more likely to die at home than men or those who lived alone. CONCLUSION: Place of death is influenced by the socio-demographic characteristics of patients, the characteristics of their caregivers, and health service factors. Palliative care programs need to tailor services to men and those living alone in order to reduce institutional deaths.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".