Building mentally healthy workplaces: developing the SUPPORT framework for values-driven leadership & business practices
Bibliographic record
Abstract
Workplace mental health and wellbeing are essential for fostering a productive, engaged, and resilient workforce. While core business values are central to the WHO Healthy Workplace Model (WHO, 2010), research on their role in workplace mental health remains limited. The SUPPORT Business Values Framework addresses this gap by identifying core values and guiding principles to support a strategic, values-driven approach to workplace wellbeing. Using a mixed-methods design, 35 organisations from the UK and Ireland participated in employee-wide surveys assessing psychosocial safety climate (PSC; Dollard & Bakker, 2010), wellbeing, and productivity. High-PSC organisations were identified using standardised PSC-4 benchmarks (Berthelsen et al., 2020) and formed the basis for best practice case studies. Semi-structured interviews with leaders and employees (n=26) were analysed using inductive thematic analysis (Braun & Clarke, 2006), revealing seven key values underpinning the SUPPORT Framework: Safety, Understanding, People-focused, Protect & Promote Workplace Health, Openness & approachability, Rest, balance & recovery, and Transparency & trust. The framework defines each of these values and their associated guiding principles. To refine the framework and explore its practical application, follow-up interviews with case study participants (n=6) and a focus group with business leaders (n=10) were conducted. Additionally, semi-structured interviews with an international panel of workplace health experts (n=10) provided empirical validation and implementation. This paper presents the empirically derived SUPPORT Framework and offers reflections on fostering values-driven leadership to enhance workplace mental health through strategic application of the framework.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".