Online Health Check for Reducing Alcohol Intake among Employees: A Feasibility Study in Six Workplaces across England
Bibliographic record
Abstract
BACKGROUND: Most hazardous and harmful drinkers are of working age and do not seek help with their drinking. Occupational health services are uniquely placed to universally screen employees across the range of socioeconomic and ethnic groups. The aim was to explore the feasibility and acceptability of offering electronic screening and brief intervention for alcohol misuse in the context of a health check in six different workplace settings. METHODS AND FINDINGS: Employees were recruited from six workplaces across England, including three local authorities, one university, one hospital and one petro-chemical company. A total of 1,254 (8%) employees completed the health check and received personalised feedback on their alcohol intake, alongside feedback on smoking, fruit and vegetable consumption and physical activity. Most participants were female (65%) and of 'White British' ethnicity (94%), with a mean age of 43 years (SD 11). Participants were mostly in Intermediate occupations (58%), followed by Higher managerial / professional (39%) and Routine and manual occupations (2%). A quarter of participants (25%) were drinking at hazardous levels (33% male, 21% female), which decreased with age. Sixty-four percent (n=797) of participants completed online follow-up at three months. Most participants were supportive of workplaces offering employees an online health check (95%), their preferred format was online (91%) and many were confident of the confidentiality of their responses (60%). Whilst the feedback reminded most participants of things they already knew (75%), some were reportedly motivated to change their behaviour (13%). CONCLUSIONS: Online health screening and personalised feedback appears feasible and acceptable, but challenges include low participation rates, potentially attracting 'worried well' employees rather than those at greatest health risk, and less acceptance of the approach among older employees and those from ethnic minority backgrounds and routine or manual occupations.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".