Disability management practices in education, hotel/motel, and health care workplaces
Bibliographic record
Abstract
BACKGROUND: The high costs and the impact of work disability have become a growing concern for workplaces. As a result, workplace disability management approaches have been developed to lower disability costs, protect the employability of workers, and promote early return to work. METHODS: A stratified random sample of 455 employers in education (n = 157), hotel/motel (n = 110), and health care (n = 188) sectors who completed a mailed Organizational Policies and Practices (OPP) questionnaire is reported. The OPP questionnaire asked questions about eight workplace disability management practices. The article examined the multi-dimensionality, internal consistency, and discriminant validity of the OPP and compares disability management practices across the three sectors. RESULTS: The OPP questionnaire showed good internal consistency (Cronbach's alpha = 0.95) and discriminant validity. A one-way analysis of variance (ANOVA) for each of the eight subscales demonstrated that there were statistically significant differences between the sectors in ergonomic practices (F (2,452) = 15.8, P < 0.001), disability case management (F (2,452) = 4.6, P < 0.01), return to work (F (2,452) = 10.3, P < 0.001), and people-oriented culture (F (2,452) = 4.5, P < 0.01). CONCLUSIONS: On examining disability management practices in education, hotel/motel, and health care sectors, the OPP seems to be a promising instrument that can be used to assess and monitor how employers are managing disability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".