Predictors of Health Service Use Over the Palliative Care Trajectory
Bibliographic record
Abstract
BACKGROUND: Health system restructuring coupled with the preference of patients to be cared for at home has altered the setting for the provision of palliative care. Accordingly, there has been emphasis on the provision of home-based palliative care by multidisciplinary teams of health care providers. Evidence suggests that these teams are better able to identify and deal with the needs of patients and their family members. Currently there is a lack of literature examining the predictors of palliative care service use for various professional service categories. OBJECTIVE: The purpose of this study was to examine the predictors of the propensity and intensity of five main health service categories in the last three months of life for home-based palliative care patients. DESIGN: This was a prospective cohort study. The predictors of service use were assessed using a two-part model, which treats the decision to use a service (propensity) and the amount of service use (intensity) as two distinct processes. Propensity was modeled using a logistic regression and intensity was modeled using ordinary least squares regression. RESULTS: The results indicate that each service category emerged with a different set of predictor variables. Common predictors of health service use across service categories were patient age and functional status. The results suggest that a consistent set of predictors across service categories does not exist, and thus the determinants of access to each service category are unique. CONCLUSION: These findings will help case managers, health administrators, and policy decision makers better allocate human resources to palliative patients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".