Effectiveness of training workplace managers to understand and support the mental health needs of employees: a systematic review and meta-analysis
Bibliographic record
Abstract
Managers are in an influential position to make decisions that can impact on the mental health and well-being of their employees. As a result, there is an increasing trend for organisations to provide managers with training in how to reduce work-based mental health risk factors for their employees. A systematic search of the literature was conducted to identify workplace interventions for managers with an emphasis on the mental health of employees reporting directing to them. A meta-analysis was performed to calculate pooled effect sizes using the random effects model for both manager and employee outcomes. Ten controlled trials were identified as relevant for this review. Outcomes evaluating managers' mental health knowledge (standardised mean difference (SMD)=0.73; 95% CI 0.43 to 1.03; p<0.001), non-stigmatising attitudes towards mental health (SMD=0.36; 95% CI 0.18 to 0.53; p<0.001) and improving behaviour in supporting employees experiencing mental health problems (SMD=0.59; 95% CI 0.14 to 1.03; p=0.01) were found to have significant pooled effect sizes favouring the intervention. A significant pooled effect was not found for the small number of studies evaluating psychological symptoms in employees (p=0.28). Our meta-analysis indicates that training managers in workplace mental health can improve their knowledge, attitudes and self-reported behaviour in supporting employees experiencing mental health problems. At present, any findings regarding the impact of manager training on levels of psychological distress among employees remain preliminary as only a very limited amount of research evaluating employee outcomes is available. Our review suggests that in order to understand the effectiveness of manager training on employees, an increase in collection of employee level data is required.
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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".