Collaborative Partnership in Age-Friendly Cities: Two Case Studies From Quebec, Canada
Bibliographic record
Abstract
This article aims to explain the collaborative partnership conditions and factors that foster implementation effectiveness within the age-friendly cities (AFC) in Quebec (AFC-QC), Canada. Based on a community-building approach that emphasizes collaborative partnership, the AFC-QC implementation process is divided into three steps: (1) social diagnostic of older adults' needs; (2) an action plan based on a logic model; and (3) implementation through collaborations. AFC-QC promotes direct involvement of older adults and seniors' associations at each of the three steps of the implementation process, as well as other stakeholders in the community. Based on two contrasting case studies, this article illustrates the importance of collaborative partnership for the success of AFC implementation. Results show that stakeholders, agencies, and organizations are exposed to a new form of governance where coordination and collaborative partnership among members of the steering committee are essential. Furthermore, despite the importance of the senior associations' participation in the process, they encountered significant limits in the capacity of implementing age-friendly environments solely by themselves. In conclusion, we identify the main collaborative partnership conditions and factors in AFC-QC.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.023 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".