Nonsmoking and Nonobesity Hiring Policies and Practices: A Comparative Analysis
Bibliographic record
Abstract
Background: There is a dearth of literature related to nonobesity-only hiring policies. Addressing this significant gap in our knowledge base would enable a better understanding of the consequences of implementing nonobesity-only hiring policies. Methods: This paper analyzed both nonobesity-only and nonsmoking-only hiring policies according to ten criteria. The ten criteria selected were based on earlier literature reviews and frameworks for analyzing nonsmoking hiring policies and practices. Findings: The similarities between nonsmoking-only and nonobesity-only hiring policies were in the prevalence and incidence of smoking and obesity, exacerbating social inequalities, privacy and discrimination, addictive properties, increasing healthcare costs and insurance premiums, and loss of productivity. The differences between the two were in hiring policy documentation, legal protection, promoting a healthy institutional image, and health consequences in the workforce. Conclusions: The most dramatic difference was that second-hand and third-hand smoke have harmful effects on nonsmoking employees (whereas obesity has no such effect on others) and that legal protection is lacking for individuals who are obese (whereas some legal protection does exist for smokers). As organizations consider implementing restrictive hiring policies and practices, considering the ten criteria offered in this paper can inform the decision-making process.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".