Social support, social media and life satisfaction with BD during the COVID-19 Pandemic (Preprint)
Bibliographic record
Abstract
BACKGROUND Reliable and consistent social support are associated with the psychological well-being of those with severe mental illness, including bipolar disorder (BD). Yet the COVID-19 pandemic and associated social distancing measures (e.g., shelter-in-place) have reduced access to regular social support. Concomitantly, use of social media during the pandemic has increased (i.e., social, non-social, passive social media usage). OBJECTIVE We set out to identify associations between BD symptoms, social support and social media use (SMU) during the pandemic. METHODS Using micro-targeted Facebook advertising, we recruited 102 adults with BD, most lived in North America (Canada = 45, USA = 15), Western Europe (e.g., UK=18, Ireland=10), South Africa (4) and Oceania (e.g., Australia=4, New Zealand=3). On average, participants were 53.96 years of age (SD = 13.22, range 20-77 years); they completed questionnaires online 32 months after the World Health Organization declared COVID-19 a global pandemic. RESULTS Consistent with previous research, symptoms of hypo/mania and depression are correlated; and symptoms of depression predict loneliness, social support and life satisfaction. Social support predicts social Facebook usage whereas passive Facebook use predicts life satisfaction. Symptoms of depression emerged as indirect predictors of SMU via social support. CONCLUSIONS Most participants (60.8%) reported accessing social media several times a day during the pandemic, and 36% reported using social media more often since the emergence of COVID-19. Our findings suggest bidirectional associations between social media use and well-being with BD. Longitudinal data collection is required to identify associations between BD symptoms, social media use and well-being over time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".