From Wasteland to Bloom: Exploring the Organizational Profiles of Occupational Health and Well-Being Strategies and Their Effects on Employees’ Health and Well-Being
Bibliographic record
Abstract
Based on the signaling and conservation of resources theories, this study aims to identify different strategic organizational profiles related to occupational health and well-being (OHWB). Additionally, this study explores how these various organizational profiles impact employees' well-being, specifically in relation to absenteeism, emotional exhaustion, work overload, intention to quit, and job satisfaction. Data were collected from 59 organizations and 2828 employees. The first phase of this study presents the latent profile analysis carried out to identify OHWB organizational profiles. This analysis reveals four organizational profiles that are metaphorically named according to the growth stages of plants (i.e., wasteland, sprouting, budding, and blooming OHWB profiles). The second phase of this study investigates the associations between the latent profiles assigned to the organizations with absenteeism, intention to quit, emotional exhaustion, feelings of work overload, and job satisfaction among their employees using MANOVA. The results show that organizational profiles influence employees' health and well-being. Employees working in organizations with a low OHWB profile, known as the "wasteland profile", tend to report more days of absenteeism, higher levels of emotional exhaustion, greater work overload, and lower job satisfaction. Employees are also more likely to express a greater intention to quit their jobs than those working in organizations with a higher OHWB profile (a "blooming profile"). This study is useful for organizations and practitioners seeking to understand how investing in a health and well-being strategy can benefit their employees.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".