Arrimage entre le RSSS et les municipalités dans le programme MADA : une analyse selon un modèle de la gouvernance collaborative
Bibliographic record
Abstract
Research Framework: The population of Quebec is ageing particularly fast, yet people over the age of 65 tend to receive less support than other age groups (Maltais, 2018). Older people form a group at higher risk of being socially isolated, which can lead to serious health problems (Holt-Lunstad et al., 2017). These problems require the creation of national programs to ensure their well-being such as the Age-Friendly Municipality Initiative (AFMI). However, the partnership between actors from municipalities and the Health and Social Services Network (HSSN) is flawed and undermines the initiative’s success.Objectives: Present obstacles that harm the intersectoral partnership in the AFMI between actors from municipalities and the HSSN, using the Ansell and Gash’s (2007) collaborative governance model, then reveal avenues of intervention based on certain experiments. Their analysis may help in the enhancement of intersectoral partnership and eventually help to meet the needs of seniors. Methodology: To illustrate the partnership between actors from municipalities and the HSSN, a qualitative methodology was used via three methods: 1) a brief literature review; 2) research results from the Quebec’s Age-Friendly Cities Research Team; 3) interviews conducted with actors from the field (strategic, administrative and operational level).Results: The interviews allowed the authors to identify obstacles in the intersectoral partnership in terms of starting conditions, based on the Ansell and Gash’s (2007) collaborative governance model. They also helped to identify certain experiments to guide new avenues of intervention and restore the intersectoral partnership. Conclusion: The partnership between actors from municipalities and the HSSN must be enhanced to allow an appropriate response to older people’s needs in the AFM process.Contribution: The analysis based on Ansell and Gash’s model identifies a weakness in the intersectoral partnership in the AFM process. The experiments proposed may inspire new promising practices on this subject.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".