Who initiates and organises situations for work-related alcohol use? The WIRUS culture study
Bibliographic record
Abstract
AIMS: Alcohol is one of the leading causes of ill health and premature death in the world. Several studies indicate that working life might influence employees' alcohol consumption and drinking patterns. The aim of this study was to explore work-related drinking situations, with a special focus on answering who initiates and organises these situations. METHODS: Data were collected through semi-structured group interviews in six Norwegian companies from the private ( n=4) and public sectors ( n=2), employing a total of 3850 employees. The informants ( n=43) were representatives from management and local unions, safety officers, advisers from the social insurance office and human-resource personnel, health, safety and environment personnel, and members from the occupational environment committee. Both qualitative and quantitative content analyses were applied in the analyses of the material. RESULTS: Three different initiators and organisers were discovered: the employer, employees and external organisers. External organisers included customers, suppliers, collaborators, sponsors, subcontractors, different unions and employers' organisations. The employer organised more than half of the situations; external organisers were responsible for more than a quarter. The differences between companies were mostly due to the extent of external organisers. CONCLUSIONS: The employer initiates and organises most situations for work-related alcohol use. However, exposure to such situations seems to depend on how many external relations the company has. These aspects should be taken into account when workplace health-promotion initiatives are planned.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".