Digital Training Program for Line Managers (Managing Minds at Work): Protocol for a Feasibility Pilot Cluster Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Mental health problems affect 1 in 6 workers annually and are one of the leading causes of sickness absence, with stress, anxiety, and depression being responsible for half of all working days lost in the United Kingdom. Primary interventions with a preventative focus are widely acknowledged as the priority for workplace mental health interventions. Line managers hold a primary role in preventing poor mental health within the workplace and, therefore, need to be equipped with the skills and knowledge to effectively carry out this role. However, most previous intervention studies have directly focused on increasing line managers' understanding and awareness of mental health rather than giving them the skills and competencies to take a proactive preventative approach in how they manage and design work. The Managing Minds at Work (MMW) digital training intervention was collaboratively designed to address this gap. The intervention aims to increase line managers' knowledge and confidence in preventing work-related stress and promoting mental health at work. It consists of 5 modules providing evidence-based interactive content on looking after your mental health, designing and managing work to promote mental well-being, management competencies that prevent work-related stress, developing a psychologically safe workplace, and having conversations about mental health at work. OBJECTIVE: The primary aim of this study is to pilot and feasibility test MMW, a digital training intervention for line managers. METHODS: We use a cluster randomized controlled trial design consisting of 2 arms, the intervention arm and a 3-month waitlist control, in this multicenter feasibility pilot study. Line managers in the intervention arm will complete a baseline questionnaire at screening, immediately post intervention (approximately 6 weeks after baseline), and at 3- and 6-month follow-ups. Line managers in the control arm will complete an initial baseline questionnaire, repeated after 3 months on the waitlist. They will then be granted access to the MMW intervention, following which they will complete the questionnaire post intervention. The direct reports of the line managers in both arms of the trial will also be invited to take part by completing questionnaires at baseline and follow-up. As a feasibility pilot study, a formal sample size is not required. A minimum of 8 clusters (randomized into 2 groups of 4) will be sought to inform a future trial from work organizations of different types and sectors. RESULTS: Recruitment for the study closed in January 2022. Overall, 24 organizations and 224 line managers have been recruited. Data analysis was finished in August 2023. CONCLUSIONS: The results from this feasibility study will provide insight into the usability and acceptability of the MMW intervention and its potential for improving line manager outcomes and those of their direct reports. These results will inform the development of subsequent trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT05154019; https://clinicaltrials.gov/study/NCT05154019. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48758.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.083 | 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".