The CoVivre Program: Community Development and Empowerment to Address the Inequalities Exacerbated by the COVID-19 Pandemic in the Greater Montreal Area, Canada
Bibliographic record
Abstract
The COVID-19 pandemic had devastating effects around the world, yet it was not experienced equally by all. The emergence of the virus has been linked with the intensification of discrimination and inequities, as well as other systemic issues already present in society prior to the pandemic. The CoVivre Program was created with the mission to facilitate and accelerate initiatives aimed at reducing socioeconomic and health disparities caused by the COVID-19 pandemic in the Greater Montreal Area. CoVivre aims to inform, protect, and support communities, with an emphasis on communities experiencing marginalization, such as ethnic and religious minorities, refugees, asylum seekers, and precarious workers. This mission is guided by the latest research and CoVivre’s values of community empowerment, partnership, democratic communications, and cultural competency, among others. This article describes the process of planning and implementing the program and its components: Communications, Outreach and Awareness Raising, and Psychosocial Support and Mental Health, with a description of one project per component. It also aims to identify obstacles and facilitators of the program, to reflect on their relation with local and global ecosystems and their relationship to community action, and to examine community mobilization as expressing both resilience and resistance to top-down impositions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".