Eudaemonic bridges: Using an interdisciplinary approach to foster physical health and well-being in the workplace
Bibliographic record
Abstract
Dataset for the paper entitled: Anxiety and the severity of Tension-Type Headache mediate the relation between headache presenteeism and workers’ productivity. - Under revision in PLOS One. Abstract: The primary objective of this study was to explore the mechanisms and conditions whereby Tension-Type Headache (TTH) presenteeism relates to health-related loss of productivity as a result of both reduced physical and mental health. To this end, Structural Equation Modeling (SEM) was used to conduct a secondary data analysis of a randomized clinical trial involving 78 Tension-type Headache (TTH) patients. The results showed that TTH presenteeism did not directly relate to health-related loss of productivity, either due to physical, or mental health problems. However, through anxiety-state, TTH presenteeism decreased patients’ productivity, as consequence of reduced physical and mental health. Moreover, by increasing the severity of the Tension-Type Headache, TTH presenteeism indirectly decreased patients’ productivity as consequence of reduced physical health (but not mental health). Finally, our results show that such indirect effects only occur when the cause of TTH is non-mechanical (e.g., hormonal causes, etc.). Our work provides an integrative model that can inform organizational behaviorists and health professionals (e.g., physiotherapists). Implications for organizational health are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".