Larger Workplaces, People-Oriented Culture, and Specific Industry Sectors Are Associated with Co-Occurring Health Protection and Wellness Activities
Bibliographic record
Abstract
Employers are increasingly interested in offering workplace wellness programs in addition to occupational health and safety (OHS) activities to promote worker health, wellbeing, and productivity. Yet, there is a dearth of research on workplace factors that enable the implementation of OHS and wellness to inform the future integration of these activities in Canadian workplaces. This study explored workplace demographic factors associated with the co-implementation of OHS and wellness activities in a heterogenous sample of Canadian workplaces. Using a cross-sectional survey of 1285 workplaces from 2011 to 2014, latent profiles of co-occurrent OHS and wellness activities were identified, and multinomial logistic regression was used to assess associations between workplace demographic factors and the profiles. Most workplaces (84%) demonstrated little co-occurrence of OHS and wellness activities. Highest co-occurrence was associated with large workplaces (odds ratio (OR) = 3.22, 95% confidence interval (CI) = 1.15⁻5.89), in the electrical and utilities sector (OR = 5.57, 95% CI = 2.24⁻8.35), and a high people-oriented culture (OR = 4.70, 95% CI = 1.59⁻5.26). Promoting integrated OHS and wellness approaches in medium to large workplaces, in select industries, and emphasizing a people-oriented culture were found to be important factors for implementing OHS and wellness in Canadian organizations. Informed by these findings, future studies should understand the mechanisms to facilitate the integration of OHS and wellness in workplaces.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".