Impacts of heavy smoking and alcohol consumption on workplace presenteeism
Bibliographic record
Abstract
ABSTRACT: Presenteeism refers to the practice of going to work despite poor health, resulting in subpar performance. This study aimed to explore the impacts of smoking and alcohol consumption on workplace presenteeism based on demographic, health-related, and employment variables.The study adopted a cross sectional design with 60,051 wage workers from the database of the second and third Korean Working Conditions Surveys in 2010 and 2011, respectively. A total of 41,404 workers aged 19 years and older, who had worked for at least 1 hour in the previous week, answered the survey questions. Chi-square test as well as univariate and multiple logistic regression analyses were conducted using SPSS, version 18.0, to determine the impacts of smoking and alcohol consumption on workplace presenteeism.Of the 41,404 Korean workers, 8512 (20.6%) had experienced presenteeism in the past 12 months. There were significant differences among gender, age, educational status, income, health problems, absenteeism, shift work, night shift, weekly working hours, exposure to secondhand smoke at work, and satisfaction with the workplace environment. Based on the results of multiple regression analysis, heavy smoking (adjusted odds ratio = 1.38, 95% confidence intervals [1.11, 1.72]) and high-risk drinking (adjusted odds ratio = 1.19, 95% confidence intervals [1.08, 1.31]) were significantly related to presenteeism among workers.The results of our study confirmed that smoking and alcohol drinking were related to presenteeism even after controlling other variables (demographic, health-related, and employment variables) that affect presenteeism. Smoking and alcohol drinking are associated with and potentially influence presenteeism; in particular, heavy smoking and high-risk drinking contributed to presenteeism. Companies that encourage employees to receive treatments for reduction of smoking or alcohol consumption may benefit from greater productivity. Hence, we should consider the impact of smoking and alcohol consumption in the workplace and build appropriate strategies and programs to help reduce these behaviors.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".