Regional Variation in Palliative Care Receipt in Ontario, Canada
Bibliographic record
Abstract
Background: Delivery of health services in the province of Ontario is organized into 14 Local Health Integration Networks (LHINs), and further into 76 LHIN subregions, making these a natural unit of comparing the regional differences in palliative care receipt among decedents who were identified as having palliative care needs. Objective: To assess the presence and magnitude of the remaining regional variation in palliative care receipt in Ontario after accounting for demographic and socioeconomic differences between the LHIN subregions, and therefore to assess whether the standardized proportion of palliative care receipt as a performance indicator can capture potential performance-related issues. Design: A retrospective cohort study based on Ontario administrative data sources. Setting/Subjects: Ontario residents who died between April 1, 2015 and March 31, 2016 and were identified as having palliative care needs. Measurements: Date of death, diagnostic codes used for determining palliative care needs, and services receipt in last year of life were identified from multiple administrative databases. Demographic and socioeconomic information were derived from linking decedents' postal codes to Statistics Canada Census data and Ontario Marginalization Index. Results: Statistically significant variation ranging from 63% to 75% in palliative care receipt exists between Ontario subregions even after accounting for demographic and socioeconomic differences, including age, sex, rurality, income quintile, and the four dimensions of the Ontario Marginalization Index. Conclusions: Annual directly standardized proportion of palliative care receipt can be used as a performance indicator to detect regional differences in service receipt while adjusting for regional differences in the characteristics of the decedent populations. The factors to be adjusted for can be chosen based on the comparison of interest.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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".