Examining Associations Between Social Networking Site Alcohol-Specific Social Norms, Posting Behavior, and Drinking to Cope During the COVID-19 Pandemic
Bibliographic record
Abstract
Emerging research suggests that there may be important links between social networking site (SNS) use and alcohol consumption specific to COVID-19. In addition, substantial research indicates that descriptive normative perceptions are a strong predictor of drinking behavior more generally. However, less is known about SNS-specific norms and how they might be associated with health-related behavior. Thus, the primary goal of this study was to determine whether descriptive normative perceptions for alcohol posting related to COVID-19 on SNSs are associated with both personal SNS posting behavior and drinking to cope with COVID-19-related stress, among a sample of 587 adults (48.4 percent women; mean age = 48.7 years) across the United States. All study procedures were approved by the local IRB. Results indicate that perceiving same-age peers to be posting on SNSs about their alcohol use to cope with pandemic-related stress/boredom is associated with both an increased likelihood of making such postings oneself and increased drinking to cope with the pandemic. Results have important implications for prevention and intervention efforts aimed to curb risky drinking during the pandemic and suggest that SNS behavior and norms should be incorporated into these strategies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".