Distress and drinking: Cross-cultural connections and contexts
Bibliographic record
Abstract
Wilsnack, R., Kristjanson, A. F., Wilsnack, S. C. & Benson, P. W. (2012). Distress and drinking: Cross-cultural connections and contexts. International Journal of Alcohol and Drug Research, 1(1), 79-94. doi: 10.7895/ijadr.v1i1.37 (http://dx.doi.org/10.7895/ijadr.v1i1.37)Aims: Research on how distress is related to drinking has paid relatively little attention to gender and to cultural differences. This study examines how distress is associated with men’s and women’s drinking cross-culturally.Design: Cross-sectional survey.Setting/Participants: Surveys of 30,728 women and 24,204 men in 22 countries of the GENACIS project (Gender, Alcohol and Culture: An International Study) provided data on how women’s and men’s mental, interpersonal and work-situational distress are related to their drinking patterns.Measurements: Analyses examined correlations within surveys, and used hierarchical linear modeling (HLM) to take into account economic development, abstinence rates and distress levels in the populations surveyed.Findings: We found few associations of drinking patterns with reported stressful work situations. Mental and interpersonal distress had more frequent but geographically scattered associations with men’s and women’s drinking, particularly with quantities consumed. HLM analyses confirmed cross-culturally that drinking tended to increase with psychological and interpersonal distress, but the societal-level variables had few effects.Conclusions: Distress measures in GENACIS surveys were positively though not powerfully associated with both women's and men's drinking cross-culturally, associations not attributable to societal-level characteristics. The findings indicate a need for better cross-cultural information about the processes by which distress may lead to heavier drinking.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".