Socioeconomic Differences in and Predictors of Home-Based Palliative Care Health Service Use in Ontario, Canada
Bibliographic record
Abstract
The use of health services may vary across people with different socioeconomic statuses, and may be determined by many factors. The purposes of this study were (i) to examine the socioeconomic differences in the propensity and intensity of use for three main home-based health services, that is, home-based palliative care physician visits, nurse visits and personal support worker (PSW) hours; and (ii) to explore the determinants of the use of home-based palliative care services. A prospective cohort study was employed. A total of 181 caregivers were interviewed biweekly over the course of the palliative care trajectory, yielding a total of 994 interviews. The propensity and intensity of health service use were examined using logistic regression and negative binomial regression, respectively. The results demonstrated that both the propensity and intensity of home-based nurse and PSW visits fell with socioeconomic status. The use of home-based palliative care services was not concentrated in high socioeconomic status groups. The common predictors of health service use in the three service categories were patient age, the Palliative Performance Scale (PPS) score and place of death. These findings may assist health service planners in the appropriate allocation of resources and service packages to meet the complex needs of palliative care populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".