Urgent or just Important?: Mental Wellbeing Training and the Need for Multilevel Support and Tangible Organisational Commitment
Notice bibliographique
Résumé
Poor mental health is now cited alongside back pain as one of the two major occupational health issues which are most costly to UK business in terms of sickness absence and lost productivity. This appeal to organisations’ bottom line has prompted a variety of strategies, preventative measures and good practice case studies around how employers mental wellbeing can be promoted. The COVID-19 pandemic has accelerated this conversation, with flexible working policies and other provisions introduced at speed both to counteract the mental impact of lockdown and also to ensure the steady functioning of those organisations equipped for a work-from-home model. However, organisations wishing to proactively invest in improved employee wellbeing may face ambivalence or unintended negative consequences if the provisions are viewed as tokenistic, or place increased onus on the employees without commensurate adjustment of policy and organisation-wide practice. iAmAWARE is an online platform, co-developed by charity, academic and frontline employee partners, providing access to psychoeducation and stress reduction training. We sought to involve prospective user from the design phase through to data interpretation. First, we carried out focus groups in two business settings and at three key organisational level: leadership, human resource management and operational or customer-facing. These were aimed at elucidating the operating understanding of mental health and wellbeing in the organisations, baseline levels of wellbeing policy and provision, and expectations for online training. Following a Participatory Theme Elicitation (PTE) protocol, a different set of workers worked with the research team to analyse and interpret the focus group data. These emergent themes are presented alongside survey responses from users of the pilot iAmAWARE programme, which was rolled out following COVID-19 lockdown of March 2020. Open text survey items asked participants about the impact of the pandemic and any benefits they gained from the iAmAWARE training while working from home. Focus groups and the subsequent participatory analysis framework stimulated repeated reference to organisational systems and hierarchies. Members inferred from colleagues’ responses a nervousness and taboo about raising mental wellbeing issues, for fear this might suggest they were ‘not up to it’. For some staff, seeking workplace accommodations or workload relief could represent a challenge to the authority and competence of senior leadership and thus be seen as too costly. iAmAWARE was viewed positively as engaging and accessible, though employees recommended greater personalisation and visibility of their own organisational leaders, including a message that engagement with the programme should inform ongoing re-evaluation of work culture. The results of this co-design study suggest that symbolic resonance of workplace wellbeing programmes are as important to consider as the properties of the programmes themselves. An organisational welfare and solidarity framing may garner more sustainable buy-in from frontline staff, but this requires tangible evidence that the organisation is listening and concerned not just with the symptoms, but in its own role in shaping employee wellbeing or the lack thereof.
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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 | 0,011 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,009 |
| Communication savante | 0,010 | 0,007 |
| Science ouverte | 0,001 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
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 ».