Smoke-Free Workplace Policies and Organizational Attraction
Bibliographic record
Abstract
Companies adopt smoke-free workplace policies to improve health of their employees, but how severely suchpolicies are enforced can have an impact on non-smoking employees as well and can also affect employees' viewabout their companies. The current study examined the extent to which perceived severity of and organizationalsupport for a smoke-free workplace policy affected employees’ attraction toward their organizations. The datafrom 621 employees of 20 companies in the U.S. and 27 companies in Korea showed that the extent to whichemployees considered a smoke-free policy at their workplace to be enforced severely was negatively related toorganizational attraction (coefficient = –0.22, p = .002) and perceived organizational support was positivelyrelated to organizational attraction (coefficient = 0.41, p< .001). The negative relationship between perceivedseverity and organizational attraction, however, became weaker for organizations that had employees with higherperceptions of organizational support. In contrast to smokers (coefficient = –.05), ex-smokers' perceived severityof a smoke-free policy was positively related to their organizational attraction (coefficient = .31). These findingsindicated that a smoke-free policy in the workplace can have implications for non-smokers, includingex-smokers, as well as for smokers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".