Multilevel Governance and the Implementation of Inclusion Policy for Older Adults With Intellectual and Developmental Disabilities in Canada
Bibliographic record
Abstract
Abstract This study analyzes the influence of Canadian provincial governance structures on support services for older adults with intellectual and developmental disabilities. Following multilevel governance (MLG) literature, it compares Ontario's more centralized governance structure with Québec's more disentangled multi‐jurisdictional structure by how they promote social inclusion—a guiding policy priority for disability and aging services. Despite their divergent MLG structures, the findings show that when accounting for implementation effects, Ontario and Québec are producing strikingly similar social inclusion outcomes, owing to the similar motivations of frontline workers. Interviewees in both provinces prioritized needs related to health and medication use, to the detriment of social inclusion goals, reflecting the “siloization” of aging and disability support services. Respondents also stressed the impracticality of person‐centered care within current funding models, thus defending divergence from a central policy priority in both provinces. This points to the importance of accounting for the input of policy implementers, whose resistance to overarching policy priorities confound structural differences in the production of policy outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".