Enhanced Tobacco Control Initiative at Johns Hopkins Health System: Employee Fairness Perception
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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 it