Inequities in home care use among older Canadian adults: Are they corrected by public funding?
Bibliographic record
Abstract
BACKGROUND: Although care use should parallel needs, enabling and predisposing circumstances including the socio-demographic inequities of socioeconomic status (SES), gender, or isolation often intervene to diminish care. We examine whether availability of state-funded medical and support services at home can rebalance these individual and social inequities, and do this by identifying if and how intersecting social identities predict homecare use among older Canadian adults. METHODS: Using the Canadian Longitudinal Study on Aging (CLSA) of 30,097 community-dwelling adults aged 45 to 85, we performed recursive partitioning regression tree analysis using Chi-Squared automatic interaction detection (CHAID). Combinations of individual and social characteristics including sociodemographic, family-related, physical and psychological measures and contextual indicators of material and social deprivation were explored as possible predictors of formal and informal care use. RESULTS: Diminished function i.e. increased need, indicated by Activities of Daily Living, was most strongly aligned with formal care use while age, living arrangement, having no partner, depression, self-rated health and chronic medical conditions playing a lesser role in the pathway to use. Notably, sex/gender, were not determinants. Characteristics aligned with informal care were first-need, then country of birth and years since immigration. Both 'trees' showed high validity with low risk of misclassification (4.6% and 10.8% for formal and informal care, respectively). CONCLUSIONS: Although often considered marginalised, women, immigrants, or those of lower SES utilised formal care equitably. Formal care was also differentially available to those without the financial or human resources to receive informal care. Need, primarily medical but also arising from living arrangement, rather than SES or gender predicted formal care, indicating that universal government-funded services may rebalance social and individual inequities in formal care use.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".