Why do managers allocate resources to workplace health promotion programmes in countries with national health coverage?
Bibliographic record
Abstract
There is extensive evidence that worksite health promotion (WHP) programmes reduce healthcare costs and improve employee productivity. In many countries, a large proportion of healthcare costs are borne by the state. While the full benefits of WHP are still created, they are shared between employers and the state, even though the employer bears the full (after-tax) cost. Employers therefore have a lower incentive to implement WHP activity. We know little about the beliefs of managers with decision responsibility for the approval and implementation of WHP programmes in this context. This article reports the results of a study of the attitudes of Canadian senior general managers (GMs) and human resource managers (HRMs) in the auto parts industry in Ontario, Canada towards the consequences of increasing discretionary spending on WHP, using Structural Equation Modelling and the Theory of Planned Behaviour. We identified factors that explain managers' intentions to increase discretionary spending on wellness programmes. While both senior GMs and HRMs are motivated primarily by their beliefs that WHP reduces indirect costs of health failure, GMs were also motivated by their moral responsibility towards employees (but surprisingly HRMs were not). Importantly, HRMs, who usually have responsibility for WHP, felt constrained by a lack of power to commit resources. Most importantly, we found no social expectation that organizations should provide WHP programmes. This has important implications in an environment where the adoption of WHP is very limited and cost containment within the healthcare system is paramount.
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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 | 0.004 | 0.020 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".