How Well We Know Wellness: Closing the Gap on Wellness Program Research in the Workplace Through Mixed-methods Topic Modelling
Bibliographic record
Abstract
This research brings together a systematic literature review, topic modeling, and qualitative analysis in a novel, mixed method approach to synthesize research on workplace wellness interventions and offer directions for future research. First, we conducted a systematic literature review to create a corpus of 5,674 research publications on wellness intervention research. Next, topic modeling, a quantitative machine learning technique that identifies key topics, was deployed to extract trends within the corpus. Finally, abstracts were qualitatively coded according to a framework that examined: 1) journal outlets and disciplines publishing workplace wellness intervention research; 2) different types of wellness interventions that are studied in workplaces; 3) dependent variables measured as outcomes of wellness interventions, and any reported mechanisms or theories of change linking the intervention to the outcomes; 4) methodologies utilized by researchers and their corresponding rigor; and 5) context the research takes place within. Interestingly, despite growing recognition that wellness is multi-dimensional, ‘wellness’ is increasingly operationalized using primarily psychological outcomes. Other trends depicted a lack of consistency in research design, program theory, and results making it difficult to specify how or why a program is beneficial for a specific population and setting, and the extent to which results were reliable.
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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 | 0.220 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".