Emergent Issues in Directly-Funded Care: Canadian Perspectives
Bibliographic record
Abstract
Direct Funding (DF) provides individuals with a budget to arrange their own home care instead of receiving publicly arranged services. DF programs have evolved in a number of countries since the 1970s. In Canada, while small-scale DF programs have existed since the early 1970s, the research on these programs remains limited. Responding to gaps identified by an umbrella review and using a health equity framework, this research extends the knowledge base on DF programs from a Canadian perspective through an environmental scan. The research asks: What are the features of DF programs across Canada? What are the emerging issues related to program design and policy development? The study employed a qualitative environmental scan design, gathering data through questionnaires and semi-structured interviews (n = 23). The findings include a summary table describing features of 20 programs and two interview themes: a lack of information on DF workers and concerns about the growing role of home care agencies. This study has the potential to contribute to long-term health equity monitoring research. The findings suggest that as DF expands in Canada, promoting hiring from personal networks may address inequities in rural access to home care services and improve social outcomes for linguistic, cultural, and sexual minorities. However, the findings underscore a need to monitor access to DF programs by people of lower-socioeconomic backgrounds in Canada and discourage policy design that requires independent self-management, which disadvantages people with compromised decision-making capacities.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.048 | 0.026 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".