Occupational justice in direct-funded attendant services: Possibilities and constraints
Bibliographic record
Abstract
In direct-funded attendant services, public funds are transferred to service users to recruit, hire, train, and manage support staff. An outcome of activist efforts to deinstitutionalize support services, direct funding models are often said to increase the choice and control that disabled people have in their attendant services and in everyday life. Focusing on one direct funding program in Ontario, Canada, the overarching reflexive ethnographic study explored the work that physically disabled adults contribute to organize and manage their own attendant services. In this paper we present a secondary analysis of qualitative interview data, examining the impact of participation in direct funding on service users’ occupational engagement. The results of this analysis suggest that direct funding promotes occupational rights by providing supports needed to participate in a range of meaningful occupations. At the same time, occupational engagement was found to be constrained at individual and group levels. In this paper we apply occupational justice concepts to analyze systemic constraints on occupational engagement and occupational choice, including program eligibility criteria and societal factors that condition access to and use of public resources. The discussion highlights how direct funding can support occupational engagement and constrain occupational choice, illuminating the complexity of occupational justice as a dynamic concept that may be promoted and obstructed simultaneously within a given context. We situate direct funding within an ongoing history of institutionalized supports for disabled people and identify specific factors that may promote or inhibit the occupational rights of disabled people who use direct-funded attendant services.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.048 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.002 | 0.003 |
| 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".