Digital Training Program for Line Managers (Managing Minds at Work): Protocol for a Feasibility Pilot Cluster Randomized Controlled Trial
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,009 | 0,004 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,083 | 0,011 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».