Developing community resilience in the face of COVID-19: case study from the Estrie region, Canada
Bibliographic record
Abstract
The COVID-19 pandemic undeniably impacted population health and several aspects of community organization, including service delivery and social cohesion. Intersectoral collaboration and equity, two key dimensions of community resilience, proved central to an effective and equitable response to the pandemic. Yet the factors that enabled or constrained communities' capacity to enact intersectoral collaboration and equity-focused action in such times of urgency and uncertainty remain poorly understood. This descriptive qualitative study aimed to (1) describe the processes through which intersectoral collaboration and equity-focused action were deployed during the first wave of COVID-19 and (2) identify factors enabling and constraining these processes. We conducted semi-directed interviews with 35 representatives of the governmental, institutional, and public and third sectors from four municipal regional counties of the Estrie region (Québec, Canada). We coded detailed interview notes following a codebook thematic analysis approach. We identified three processes through which intersectoral collaboration and equity-focused action were deployed: (1) networking; (2) adaptation, creation and innovation; and (3) human-centred action. Examples of levers which supported the deployment of these processes included capitalizing on pre-existing networks, adapting practices and services, and investing in solidarity and mutual aid. The influencing factors we describe represent concrete targets for resilience-building action. Although focused on the COVID-19 pandemic, our findings are relevant to other types of health, social, environmental or economic crises, and may guide health promotion and community development practitioners towards more effective community resilience-building responses.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.006 |
| 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".