Size Matters: A Latent Class Analysis of Workplace Health Promotion Knowledge, Attitudes, Practices and Likelihood of Action in Small Workplaces
Bibliographic record
Abstract
Workplace health programs (WHPs) have been shown to improve employee health behaviours and outcomes, increase productivity, and decrease work-related costs over time. Nonetheless, organizational characteristics, including size, prevent certain workplaces from implementing these programs. Past research has examined the differences between small and large organizations. However, these studies have typically used a cut-off better suited to large countries such as the USA. Generalizing such studies to countries that differ based on population size, scale of economies, and health systems is problematic. We investigated differences in WHP knowledge, attitudes, and practices between organizations with under 20 employees, 20–99 employees, and more than 100 employees. In 2017–2018, a random sample of employers from 528 workplaces in Alberta, Canada, were contacted for participation in a cross-sectional survey. Latent Class Analysis (LCA) was used to identify underlying response pattern and to group clusters of similar responses to categorical variables focused on WHP knowledge, attitudes, practices and likelihood of action. Compared to large organizations, organizations with fewer than 20 employees were more likely to be members of the Medium–Low Knowledge of WHP latent class (p = 0.01), the Low Practices for WHP latent class (p < 0.001), and more likely to be members of Low Likelihood of Action in place latent class (p = 0.033). While the majority of workplaces, regardless of size, recognized the importance and benefits of workplace health, capacity challenges limited small employers’ ability to plan and implement WHP programs. The differences in capacity to implement WHP in small organizations are masked in the absence of a meaningful cut-off that reflects the legal and demographic reality of the region of study.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".