Can a publicly funded home care system successfully allocate service based on perceived need rather than socioeconomic status? A Canadian experience
Bibliographic record
Abstract
The present quantitative study evaluates the degree to which socioeconomic status (SES), as opposed to perceived need, determines utilisation of publicly funded home care in Ontario, Canada. The Registered Persons Data Base of the Ontario Health Insurance Plan was used to identify the age, sex and place of residence for all Ontarians who had coverage for the complete calendar year 1998. Utilisation was characterised in two dimensions: (1) propensity - the probability that an individual received service, which was estimated using a multinomial logit equation; and (2) intensity - the amount of service received, conditional on receipt. Short- and long-term service intensity were modelled separately using ordinary least squares regression. Age, sex and co-morbidity were the best predictors (P < 0.0001) of whether or not an individual received publicly funded home care as well as how much care was received, with sicker individuals having increased utilisation. The propensity and intensity of service receipt increased with lower SES (P < 0.0001), and decreased with the proportion of recent immigrants in the region (P < 0.0001), after controlling for age, sex and co-morbidity. Although the allocation of publicly funded home care service was primarily based on perceived need rather than ability to pay, barriers to utilisation for those from areas with a high proportion of recent immigrants were identified. Future research is needed to assess whether the current mix and level of publicly funded resources are indeed sufficient to offset the added costs associated with the provision of high-quality home care.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".