Not Aware or Don’t Dare! The Use and Efficacy of Employer-Sponsored Mental Wellbeing Programs
Bibliographic record
Abstract
Employee well-being is a matter of significant concern for both workers and organizations. While many organizations implement costly Stress Management Interventions (SMIs) to improve employee well-being, their efforts are limited by debates as to which SMIs (if any) are effective and how “effectiveness” should be evaluated. The lack of research identifying key predictors of awareness and use of different types of SMIs makes it difficult for researchers and practitioners to draw conclusions with respect to SMI efficacy. Our research addresses these gaps in our understanding by using a contextual effects perspective and a large ( n = 1627) sample of employees working in a diversity of jobs within a single organization to identify the key predictors of employee awareness and use of five available SMIs and explore the link between use of these five SMIs and perceived stress. The following conclusions are supported by the findings from this study: (1) the organizational perceived culture is a better predictor of SMI awareness than individual employee attributes or employee well-being, (2) the predictors of SMI awareness are different from those predicting use, (3) employees who would most benefit from access to SMIs are less aware of what organizational benefits are available, and (4) predictions of awareness, use, and efficacy vary depending on SMI type. Using both theory and the results from our research we propose a comprehensive framework that conceptualizes SMI efficacy as a process not an outcome.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".