Collective Action for Wellness in the Malaysian Workplace: Protocol for a Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Chronic diseases and the associated risk factors are preventable with lifestyle changes such as eating a healthier diet and being more physically active. In Malaysia, the prevalence of chronic diseases, including diabetes, hypertension, and heart diseases, has risen. In the present study, we explore the potential of co-designing and implementing a digital wellness intervention to promote socially-driven health knowledge and practices in the workplace in Malaysia, drawing on social cognitive theory, social impact theory, and social influence theory. OBJECTIVE: This study aims to co-design and assess the feasibility of a socially-driven digital health intervention to promote healthy behavior and prevent chronic diseases in a workplace in Malaysia. METHODS: This study involves two phases: (i) identifying the barriers and facilitators to healthy behaviors at work and co-designing the intervention activities with the employees, (ii) implementing and evaluating the intervention's feasibility. Phase 1 will involve qualitative data collection and analysis through semi-structured, in-depth interviews and co-design workshops with the employees, while Phase 2 will consist of a feasibility study employing quantitative measurements of health behaviors through accelerometers and questionnaires. RESULTS: This study was funded in June 2021 and ethics approval for Phase 1 was obtained from the Monash University Human Research Ethics Committee in January 2022. As of August 2022, qualitative interviews with 12 employees have been completed and the data has been transcribed and analyzed. These results will be published in a future paper with results from all Phase 1 activities. CONCLUSIONS: The study will help us to better understand the mechanisms through which digital technologies can promote socially-driven health knowledge and behaviors. This research will also result in a scalable wellness intervention that could be further tailored and expanded to other employers and social groups across the region. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/39238.
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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 | 0.048 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.018 |
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".