#Caremongering: A community-led social movement to address health and social needs during COVID-19
Bibliographic record
Abstract
BACKGROUND: To combat social distancing and stay-at-home restrictions due to COVID-19, Canadian communities began a Facebook social media movement, #Caremongering, to support vulnerable individuals in their communities. Little research has examined the spread and use of #Caremongering to address community health and social needs. OBJECTIVES: We examined the rate at which #Caremongering grew across Canada, the main ways the groups were used, and differences in use by membership size and activity. METHODS: We searched Facebook Groups using the term "Caremongering" combined with the names of the largest population centres in every province and territory in Canada. We extracted available Facebook analytics on all the groups found, restricted to public groups that operated in English. We further conducted a content analysis of themes from postings in 30 groups using purposive sampling. Posted content was qualitatively analyzed to determine consistent themes across the groups and between those with smaller and larger member numbers. RESULTS: The search of Facebook groups across 185 cities yielded 130 unique groups, including groups from all 13 provinces and territories in Canada. Total membership across all groups as of May 4, 2020 was 194,879. The vast majority were formed within days of the global pandemic announcement, two months prior. There were four major themes identified: personal protective equipment, offer, need, and information. Few differences were found between how large and small groups were being used. CONCLUSIONS: The #Caremongering Facebook groups spread across the entire nation in a matter of days, engaging hundreds of thousands of Canadians. Social media appears to be a useful tool for spreading community-led solutions to address health and social needs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".