Provincial inequities of income-related home care: receipt of formal and informal care
Bibliographic record
Abstract
Background: Although physician and hospital services are universally accessible without user charges through the stipulations of the Canada Health Act (CHA), formal home care is not included in the CHA and may be subject to user charges, which vary across provinces. The user charges may result in differential substitutability with informal care across provinces according to an individual’s income. Objectives: The objective of this research is to understand if income is related to the probability of receipt of caregiving, formal or informal, in the community (excluding institutional care). It will also be investigated if and in what measure income-related horizontal inequity exists for the receipt of formal and informal care and if this relationship varies across provinces. Methods: This secondary analysis first specified a logic regression model for predicting the use of informal care and home care. After standardizing for need, a concentration index was computed to measure horizontal inequity, which was then decomposed to understand the contributing factors to the unequal distribution in the receipt of formal home care and informal care. Results: After controlling for need, pro-poor income-related horizontal inequity exists for the receipt of formal home care and informal care. Conclusions: Income-tested provincial user charges for home care may contribute to a greater utilization of home care among the poor, but it should be further investigated if there is an unequal distribution of informal caregiver burden that results from the substitution with informal care due to these user charges.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".