Developing Best-Practice Guidelines for Improving Bus Operator Health and Retention. Part I: A Transit Workplace Health Protection and Promotion Practitioner’s Guide, Part II: Final Research Report
Bibliographic record
Abstract
Transit bus operators work in a challenging environment that can lead to negative health outcomes for the operators and high costs for transit agencies due to health care costs, absenteeism, high levels of turnover, and workers’ compensation payments. This report addresses some of the health and safety issues common throughout the transit industry. The report describes the approaches that transit organizations in the United States and Canada have taken to address the health problems faced by transit employees, including identification of many common problems and detailed practices. The report includes a Practitioner’s Guide (Part I) and an Evaluation and Return on Investment (ROI) template titled, Transit Operator Workplace Health Protection and Promotion Planning, Evaluation, and Return on Investment (ROI) (available online) for use in implementing and carrying out transit-specific programs to protect the health of bus operators and other employees. The final research report (Part II) includes the background, research approach, literature review, case examples, and detailed case studies.
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.
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 | 0.054 | 0.122 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".