The Costs and Benefits of Choice: Family Managers in Directly Funded Home Care
Bibliographic record
Abstract
Abstract Directly funded (DF) home care, or consumer directed home care, gives program users a budget to choose their own services. Set in the Canadian province of Manitoba, our study examines the local DF program “Self and Family Managed Care”, which does not allow program users to hire and pay a family member. Incorporating a disability lens into care and aging studies, we share findings from a qualitative study based on 24 semi-structured interviews with DF users. We focus on the experiences of family managers, that is, representatives acting as a decision maker for an older adult. About half of the family managers in this study care for people living with dementia or cognitive decline. We identify two main themes: 1) service flexibility in DF reduces caregiver strain, 2) family managers tend to hire agencies rather than individuals to avoid administrative burden. Our discussion highlights the costs of DF from the perspective of caregivers as administrative burden (financial paperwork, finding workers, choosing a ‘good’ agency), and the benefits as flexibility (choosing workers, trusting workers, setting schedules, assigning work). We also consider the goals of family managers to enhance quality of life and avoid long-term residential care, in contrast to younger self-managers who desire control and autonomy. We recommend that DF programs need to reduce administrative work for users, support users in making informed choices, and find better ways to support, acknowledge and value the work of family managers and substitute decision makers.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".