Interactions about Coping - Social Support during Pandemics by Brazilian Users: A Media Analysis Study
Bibliographic record
Abstract
Objective- Explore how the Brazilian online community mobilized its own coping resources during the COVID-19 pandemic to deal with mass vaccination concerns, manage and cope with personal stressors brought on by the pandemic, and seek social support. Method- The Canadian Population Health Promotion Model and the Transactional Model of Stress and Coping framed this media content analysis focusing on a socially impactful event: the authorization of COVID-19 vaccinations in Brazil. Results- The retrieval of posts (January-May 2021) found 488 contents distributed as modus operandi (n=117; 24%), coping strategies focused on emotion (n=175; 35.8%), on problem (n=40; 8.1%), on reflection (n=67; 13.7%), and offer of social support (n=89; 18.2%). Among the top-five (n=393; 80.5%) actions and coping strategies, 255 contents about coping strategies with a predominant discourse on emotion-focused coping (n=160; 63.2 %). Conclusion- Interactions sustained a feeling of connection and created a context for belonging, support, and motivation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".