Developing a transdisciplinary and adaptive framework to measure health and well-being for the workplace: the 12 competencies
Bibliographic record
Abstract
Purpose Measuring and tracking health and well-being is challenging for organizations due to a lack of education linking outcomes to interventions and a disciplinary siloing of approaches and tools. To address this, this paper aims to explore adaptive and transdisciplinary design-research methods to develop an evidence-based holistic framework to measure health and well-being. Design/methodology/approach An interdisciplinary working group of researchers from academia and industry used a combination of adaptive and transdisciplinary approaches to develop a holistic framework for measuring health and well-being. The six-stage, iterative process drew on multiple theoretical models, frameworks, leading survey tools, thematic literature review and known gaps and barriers to healthy workplaces to create broad “competence areas” supported by domains, dimensions and conceptual models. Findings Five interconnected levels known to impact health and well-being were identified, within which 12 competencies are nested. Each competency is broad enough to enable benchmarking. Detailed domains and dimensions help organizations understand what to measure and track for health and well-being and can adapt as research evolves. The framework addresses industry gaps by connecting leading and lagging indicators to allow for a more systemic approach to measuring health and well-being. Originality/value Transdisciplinary and adaptive frameworks can support academic research while enabling immediate industry application. By focusing on core indicators for well-being across different disciplines, this framework increases feasibility and understanding, enables multiple tools/methods to be used in implementation and can adapt as methods and knowledge change. This can support organizational goals such as social governance responsibilities to measure and report on health and well-being.
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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 | 0.054 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".