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Record W7135909441

Urgent or just Important?: Mental Wellbeing Training and the Need for Multilevel Support and Tangible Organisational Commitment

2021· article· en· W7135909441 on OpenAlexaff
John Moriarty, Trisha; id_orcid 0000-0003-2047-2956 Forbes, Karen; id_orcid 0000-0002-4216-6135 Galway, Patricia Gillen, Paula McFadden, Heike; id_orcid 0000-0001-8973-7954 Schröder, Mark A. Tully, Paul; id_orcid 0000-0001-6947-8916 Best

Bibliographic record

VenueResearch Portal (Queen's University Belfast) · 2021
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsQueen's University
Fundersnot available
KeywordsMental healthLine managementWorkforceFocus groupVariety (cybernetics)Unintended consequencesSet (abstract data type)Mental illnessBaseline (sea)
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.009
Scholarly communication0.0100.007
Open science0.0010.009
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.077
GPT teacher head0.389
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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