A Population-based Study of Age Inequalities in Access to Palliative Care Among Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Inequalities in access to palliative care programs (PCP) by age have been shown to exist in Canada and elsewhere. Few studies have been able to provide greater insight by simultaneously adjusting for multiple demographic, health service, and socio-cultural indicators. OBJECTIVE: To re-examine the relationship between age and registration to specialized community-based PCP programs among cancer patients and identify the multiple indicators contributing to these inequalities. METHODS: This retrospective, population-based study was a secondary data analysis of linked individual level information extracted from 6 administrative health databases and contextual (neighborhood level) data from provincial and census information. Subjects included all adults who died due to cancer between 1998 and 2003 living within 2 District Health Authorities in the province of Nova Scotia, Canada. The relationship between registration in a PCP and age was examined using hierarchical nonlinear regression modeling techniques. Identification of potential patient and ecologic contributing indicators was guided by Andersen's conceptual model of health service utilization. RESULTS: Overall, 66% of 7511 subjects were registered with a PCP. Older subjects were significantly less likely than those <65 years of age to be registered with a PCP, in particular those aged 85 years and older (adjusted odds ratio: 0.4; 95% confidence interval: 0.3-0.5). Distance to the closest cancer center had a major impact on registration. CONCLUSIONS: Age continues to be a significant predictor of PCP registration in Nova Scotia even after controlling for the confounding effects of many new demographic, health service, and ecologic indicators.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".