Action intersectorielle locale pendant la pandémie de COVID-19: une démarche de développement territorial en milieu rural
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic affected the action of collaborative networks, connecting organizations from the public, community, and private sectors. Intersectoral action is a recognized strategy for tackling complex problems and reducing social inequalities. This study aims to understand how the COVID-19 pandemic modified local intersectoral action to improve the living conditions of rural populations. METHODS: Data for this qualitative, case study were collected through semi-structured individual interviews, observation sessions, and documentary analysis. Actor-network theory was used as the theoretical framework. Data collection took place from March 2021 to June 2022. The data were processed using a thematic analysis inspired by the analytical framework. RESULTS: The pandemic disrupted local intersectoral action, hampering networking operations and promoting a sectoral approach. Strategies favouring networking (use of technology and the liaison work of collective stakeholders) made it possible to create spaces for negotiating shared interests, identifying common causes, committing players to new roles, and sharing resources. CONCLUSION: When faced with disruptions, networks can be flexible, testifying to the relevance of intersectoral action to meet the needs of the population. Even if the network was in a state of near-fragmentation before the pandemic, its reconstitution and remobilization were relatively easy for the community organizers.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".