Inequities in access to directly-funded home care in Canada: a privilege only afforded to some
Bibliographic record
Abstract
BACKGROUND: Directly-funded home care (DF) provides government funds to people who need assistance with the activities of daily living, allowing them to arrange their own services. As programs expand globally, many allow DF clients to hire home care agencies to organize their services rather than finding their own workers. In Canada, half of the DF home care programs allow users to purchase agency services. The goal of this research is to describe the role of agency providers in DF home care in Canada and consider potential equity implications for service access from the perspectives of clients and families. METHODS: Framed with intersectionality, the study included online focus groups with families and clients (n = 56) in the two Canadian provinces of Alberta and Manitoba between June 2021-April 2022. All transcripts underwent qualitative thematic analysis using open and axial coding techniques. Each transcript was analyzed by two of three possible independent coders using Dedoose qualitative analysis software. RESULTS: The article presents five thematic findings. First, the focus groups document high rates of satisfaction with the care regardless of whether the client uses agency providers. Second, agency providers mediate some of the administrative barriers and emotional strain of using DF home care, and this is especially important for family caregivers who are working or have additional care responsibilities. Third, there are out-of-pocket expenses reported by most participants, with agency clients describing administrative fees despite lower pay for the frontline care workers. Fourth, agencies are not generally effective for linguistic and/or cultural matching between workers and families. Finally, we find that DF care programs cannot compensate for a limited informal support network. CONCLUSIONS: Clients and families often intentionally choose DF home care after negative experiences with other public service options, yet the results suggest that in some Canadian contexts, DF home care is a privilege only afforded to some. Given the growing inequalities that exist in Canadian society, all public home care options must be open to all who need it, irrespective of ability to pay, degree of social support, or competence in the English language.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".