Enhanced Tobacco Control Initiative at Johns Hopkins Health System: Employee Fairness Perception
Bibliographic record
Abstract
Organizations often fail to establish a clear awareness of what employees consider fair when implementing changes to employee benefits in the workplace. In 2016, the Johns Hopkins Health System (JHHS) enhanced their tobacco control efforts. In addition to enhanced smoking cessation benefits, employees were offered an increased reduction in their insurance premiums if they were nonsmokers. To qualify for the reduction, employees participated in testing rather than relying on self-reporting as had been done in the past. The shift to testing prompted a concern by some senior management at JHHS who did not want employees to feel they were not trusted. As the program unfolded at JHHS, the four-component model of procedural justice was applied to provide a framework for reviewing the implementation of the new voluntary tobacco testing at JHHS from a fairness lens. The purpose of this article is to illustrate the application of the four-component procedural model of justice to the tobacco testing process at JHHS. As approximately 75% of employees participated in the program, the experience at JHHS can be instructive to other employers who are looking to implement changes in their workplaces and how to minimize unintended consequences with their employees.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".