A Digital Tool for Assessing Well-Being at the Workplace and in Personal Life: Development and Validation of the Quan Well-Being Index
Bibliographic record
Abstract
Background Quan is a workplace well-being digital platform that supports employees, teams, and organizations in measuring, understanding, and improving their well-being. It is important to develop a validated measurement instrument that enables users to assess and track their well-being over time. Currently, no digital measurement instrument comprehensively evaluates well-being across both personal and professional domains. Objective We detail the development and validation of the Quan Well-being Index, a new digital self-report measure for assessing well-being in personal life and at the workplace. Methods We performed 3 studies. The first study involved the conceptualization of 6 initial factors, the generation of 51 items, and the steps of face and content validity. In the second study, revised items were presented to a UK sample. In the third study, an independent UK sample completed the final assessment along with a battery of well-being and personality questionnaires. A subsample of participants from the third study retook the assessment approximately 2 weeks after initial completion. Results In the first study, after face and content validity processes, the number of items was reduced to 45. In the second study, exploratory factor analysis on data from 1020 participants (age: mean 43.06, SD 12.98 years; 525 female participants) identified a 4-factor solution with 35 items (Kaiser-Meyer-Olkin value=0.98; Bartlett test: χ2990=37063.54; P<.001), accounting for 64% of variance. The 4 factors were thrive and connect in personal life, thrive and connect at work, mental health, and physical health. In the third study, confirmatory factor analysis on data from 966 participants (age: mean 44.4, SD 12.52 years; 480 female participants) tested 4 structural models. A hierarchical model (model 1) where the general factor influenced the 4 group factors demonstrated the best fit (χ2521=3467.00; Bentler comparative fit index=0.906; Tucker-Lewis index=0.892; root mean square error of approximation=0.077; standardized root mean square residual=0.048; ΔAkaike information criterion=0.0; ΔBayesian information criterion=0.0). Internal reliability was high across subscales (Cronbach α=.88-.93; McDonald ω total=0.89-0.94; Guttman λ6=0.86-0.92). Convergent validity was demonstrated by strong correlations with the Warwick-Edinburgh Mental Well-being Scale (r=0.45-0.85; P<.001) and Flourishing-at-Work Scale (r=0.80-0.84; P<.001). Divergent validity was supported through weak or negative correlations with Big Five Personality Inventory traits (eg, neuroticism: r=–0.29; P<.001). Test-retest reliability assessed in a subset of 275 participants (age: mean 52.12, SD 9.56 years; 170 female participants) over a 2-week interval was strong to very strong across factors (r=0.74-0.81; P<.001). Conclusions The Quan Well-being Index provides a comprehensive assessment of well-being at the workplace and in personal life, and is anticipated to be a valuable digital tool, enabling individuals, teams, and organizations to gain insights, monitor progress, and implement appropriate interventions for a healthier workforce.
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 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".