Cross‐National Comparisons and Correlates of Harms From the Drinking of People With Whom You Work
Bibliographic record
Abstract
BACKGROUND: While research in high-income countries (HICs) has established high costs associated with alcohol's harm to others (AHTO) in the workplace, scant attention has been paid to AHTO in the workplace in lower- or middle-income countries (LMICs). AIM: To compare estimates and predictors of alcohol's impacts upon coworkers among workers in 12 countries. METHODS: Cross-sectional surveys from 9,693 men and 8,606 women employed in Switzerland, Australia, the United States, Ireland, New Zealand, Chile, Nigeria, Lao PDR, Thailand, Vietnam, India, and Sri Lanka. Five questions were asked about harms in the past year because of coworkers' drinking: Had they (i) covered for another worker; (ii) worked extra hours; (iii) been involved in an accident or close call; or had their (iv) own productivity been reduced; or (v) ability to do their job been affected? Logistic regression and meta-analyses were estimated with 1 or more harms (vs. none) as the dependent variable, adjusting for age, sex, rurality of location, and the respondent worker's own drinking. RESULTS: Between 1% (New Zealand) and 16% (Thailand) of workers reported that they had been adversely affected by a coworker's drinking in the previous year (with most countries in the 6 to 13% range). Smaller percentages (<1% to 12%) reported being in an accident or close call due to others' drinking. Employed men were more likely to report harm from coworkers' drinking than employed women in all countries apart from the United States, New Zealand, and Vietnam, and own drinking pattern was associated with increased harm in 5 countries. Harms were distributed fairly equally across age and geographic regions. Harm from coworkers' drinking was less prevalent among men in HICs compared with LMICs. CONCLUSIONS: Workforce impairment because of drinking extends beyond the drinker in a range of countries and impacts productivity and economic development, particularly affecting men in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".