The economic impact of workplace wellness programmes in Canada
Bibliographic record
Abstract
BACKGROUND: The economic benefits of workplace wellness programmes (WWPs) are commonly cited as a reason for employers to implement such programmes; however, there is limited evidence outside of the US context exploring their economic impact. US evidence is less relevant in countries such as Canada with universal publicly funded health systems because of the lower potential employer savings from WWPs. AIMS: To conduct a systematic review of the Canadian literature investigating the economic impact of WWPs from an employer perspective. The quality of that evidence was also assessed. METHODS: We reviewed literature which included analyses of four economic outcomes: return on investment calculations; cost-effectiveness or cost-benefit analyses; valuations of productivity, turnover, absenteeism and/or presenteeism costs; and valuations of health care utilization costs. We applied the British Medical Journal (BMJ) Economic Evaluation Working Party Checklist to evaluate the quality of this evidence. RESULTS: Eight studies met the inclusion criteria. Although the studies showed that WWPs generated economic benefits from an employer perspective (largely from productivity changes), none of the reviewed studies were in the high-quality category (i.e. fulfilled at least 75% of the checklist criteria) and most had severe methodological issues. CONCLUSIONS: Though the Canadian literature pertaining to the economic impact of WWPs spans over three decades, robust evidence on this topic remains sparse. Future research should include a comparable control group, a time horizon of over a year, both direct and indirect costs, and researchers should apply analytical techniques that account for potential selection bias.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".