The impact of the Caremongering social media movement: A convergent parallel mixed‐methods study
Bibliographic record
Abstract
Public health responses to the COVID-19 pandemic, such as business restrictions, social distancing and lockdowns, had social and economic impacts on individuals and communities. Caremongering Facebook groups spread across Canada to support vulnerable individuals by providing a forum for sharing information and offering assistance. We sought to understand the specific impacts of Caremongering groups on individuals 1 year after the pandemic began. We used a convergent parallel mixed-methods approach that included semi-structured interviews with group moderators from 16 Caremongering groups and survey data from 165 group members. We used a constant comparative approach for thematic analysis of interview transcripts and open-ended text responses to the survey. We used source theme tables as joint displays to integrate interview and survey findings. Our results revealed five major themes: providing food, sharing information, supporting health and wellness, acquiring goods and services (non-food), and connecting communities. Respondents of our survey tended to be 35-65 years of age range, but reported helping adults of all ages. Our findings illustrate the potential of using a social media platform to connect with others and provide and access support. The Caremongering initiative demonstrates a community-driven, social media solution to issues such as isolation, loneliness and community health promotion.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".