The difficult reform of long-term care policies. About contradictory norms in the promotion of autonomy for older adults
Bibliographic record
Abstract
With 22.8% of the population aged 65 and over in 2021 and a 19.1% increase in this population since 2016, New Brunswick ranks second among the most aging provinces in Canada, after Newfoundland and Labrador. As in many post-industrial economies, the response to an aging population is an increase in social and health spending. In a context oftension between growing needs and budgetary constraints, the New Brunswick model has been the subject of much criticism, so much so that the ways of regulating long-term care have undergone several transformations over the last twenty years. While there is now broad consensus on the lack of solutions to meet the growing and evolving needs of the aging population, what structural and cultural difficulties related to the modes of regulation of long-term care do these reforms highlight?Based on a corpus of 50 interviews with actors involved in the design and implementation of public policies, this paper proposes to revisit two interrelated issues. On the one hand, the influence of private actors at the same time as their fragmentation in a context of increasing needs seems to weaken the legitimacy of the norms structuring the sector. On the other hand, the contradictions in the international norms and references on which the actors are led to rely are likely to weaken the legitimacy and coherence of the proposed changes.
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 | 0.045 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.056 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".