Healthy and productive workers: using intervention mapping to design a workplace health promotion and wellness program to improve presenteeism
Bibliographic record
Abstract
BACKGROUND: Presenteeism is a growing problem in developed countries mostly due to an aging workforce. The economic costs related to presenteeism exceed those of absenteeism and employer health costs. Employers are implementing workplace health promotion and wellness programs to improve health among workers and reduce presenteeism. How best to design, integrate and deliver these programs are unknown. The main purpose of this study was to use an intervention mapping approach to develop a workplace health promotion and wellness program aimed at reducing presenteeism. METHODS: We partnered with a large international financial services company and used a qualitative synthesis based on an intervention mapping methodology. Evidence from systematic reviews and key articles on reducing presenteeism and implementing health promotion programs was combined with theoretical models for changing behavior and stakeholder experience. This was then systematically operationalized into a program using discussion groups and consensus among experts and stakeholders. RESULTS: The top health problem impacting our workplace partner was mental health. Depression and stress were the first and second highest cause of productivity loss respectively. A multi-pronged program with detailed action steps was developed and directed at key stakeholders and health conditions. For mental health, regular sharing focus groups, social networking, monthly personal stories from leadership using webinars and multi-media communications, expert-led workshops, lunch and learn sessions and manager and employee training were part of a comprehensive program. Comprehensive, specific and multi-pronged strategies were developed and aimed at encouraging healthy behaviours that impact presenteeism such as regular exercise, proper nutrition, adequate sleep, smoking cessation, socialization and work-life balance. Limitations of the intervention mapping process included high resource and time requirements, the lack of external input and viewpoints skewed towards middle and upper management, and using secondary workplace data of unknown validity and reliability. CONCLUSIONS: In general, intervention mapping was a useful method to develop a workplace health promotion and wellness program aimed at reducing presenteeism. The methodology provided a step-by-step process to unravel a complex problem. The process compelled participants to think critically, collaboratively and in nontraditional ways.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".