Acknowledging bereavement, strengthening communities: Introducing an online compassionate community initiative for the recognition of pandemic grief
Bibliographic record
Abstract
Despite public health measures and collective efforts, millions of individuals have unfortunately died from COVID-19 complications worldwide, leaving several million family members at risk of developing bereavement complications. In the Canadian province of Quebec, where substantial deaths were associated with COVID-19, we established an online support community for bereaved caregivers who lost a loved one during the pandemic. We explain how we created a community that recognized pandemic grief and advocated for its wider acknowledgment. We discuss "compassionate communities," the theoretical underpinning of our initiative, as a means to foster solidarity, normalize finitude, create and maintain a safe social space through group sharing, and challenging capitalist principles. We then describe the eight areas of activities inspired by the Charter of Pallium Canada: education and training, hospices and nursing homes, media and social media, commemoration, celebrations, artistic practices and storytelling, marginalized populations, and review and evaluate. We propose that online communities constitute a powerful space for community members to gather and advocate for greater awareness of the inequities found in end-of-life care and bereavement services, to denounce abusive situations experienced by many individuals who died from COVID-19 complications, and to fight against the lack of recognition experienced by numerous caregivers.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".