Évaluation de l’implantation d’un modèle de concertation local en santé et services sociaux
Bibliographic record
Abstract
OBJECTIVE: To adapt the supply of services to the needs of the community, a local health authority has developed a consultative model in health and social services. This approach, based on shared governance and various health promotion strategies, identifies targets, develops actions, and measures the effects of these actions. This study evaluates the implementation of this consultative model from three dimensions : describe (1) implementation of the model, (2) how user experience was taken into account to prioritize and draw up action plans, (3) favourable and unfavourable implementation conditions. METHODS: A qualitative methodology based on four data sources was used (i.e. individual interviews, focus groups, observations, analysis of reports). Content analysis was conducted on the individual interviews and focus groups. The observations and analysis of reports contributed to enhance the evaluation process. RESULTS: Valorisation of experienced-based knowledge, citizen participation, shared leadership, support from institutions or stakeholders, and the dynamism of discussion tables were favourable to implementation. Time, language, cumbersome procedures, staff instability, the recent reform of the Quebec network and inherent elements of discussion tables were unfavourable conditions. CONCLUSION: The model allows actions adapted to health and social needs of a local population and increases the sense of belonging to a community. Further efforts are required to preserve the relevance, flexibility, and dynamism of this model in a context of restructuring of the Quebec health and social services network.
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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 | 0.110 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".