Persistent socioeconomic inequalities in location of death and receipt of palliative care: A population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Providing equitable care to patients in need across the life course is a priority for many healthcare systems. AIM: To estimate socioeconomic inequality trends in the proportions of decedents that died in the community and that received palliative care within 30 days of death (including home visits and specialist/generalist physician encounters). DESIGN: Cohort study based on health administrative data. Socioeconomic position was measured by area-level material deprivation. Inequality gaps were quantified annually and longitudinally using the slope index of inequality (absolute gap) and relative index of inequality (relative gap). SETTING/PARTICIPANTS: A total of 729,290 decedents aged ⩾18 years in Ontario, Canada from 2009 to 2016. RESULTS: In 2016, the modelled absolute gap (corresponding 95% confidence interval) between the most- and least-deprived neighbourhoods in community deaths was 4.0% (2.9-5.1%), which was 8.6% (6.2-10.9%) of the overall mean (46.6%). Relative to 2009, these inequalities declined modestly. Inequalities in 2016 were evident for palliative home visits (6.8% (5.8-7.8%) absolute gap, 26.3% (22.5-30.0%) relative gap) and for physician encounters (6.8% (5.7-7.9%) absolute gap, 13.2% (11.0-15.3%) relative gap), and widened from 2009 for physician encounters only on the absolute scale. Inequalities varied considerably across disease trajectories (organ failure, terminal illness, frailty, and sudden death). CONCLUSION: Key measures of end-of-life care are not achieved equally across socioeconomic groups. These data can be used to inform policy strategies to improve delivery of palliative and end-of-life services.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | medium |
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".