Thriving from Work: Conceptualization and Measurement
Bibliographic record
Abstract
Work is a major contributor to our health and well-being. Workers' thriving is directly influenced by their job design, work environment and organization. The purpose of this report is to describe the qualitative methods used to develop the candidate items for a novel measure of Thriving from Work through a multi-step iterative process including: a literature review, workshop, interviews with experts, and cognitive testing of the candidate items. Through this process, we defined Thriving from Work as the state of positive mental, physical, and social functioning in which workers' experiences of their work and working conditions enable them to thrive in their overall lives, contributing to their ability to achieve their full potential in their work, home, and community. Thriving from Work was conceptualized into 37 attributes across seven dimensions: psychological, emotional, social, work-life integration, basic needs, experience of work, and health. We ultimately identified, developed and/or modified 87 candidate questionnaire items mapped to these attributes that performed well in cognitive testing in demographically and occupationally diverse workers. The Thriving from Work Questionnaire will be subjected to psychometric testing and item reduction in future studies. Individual items demonstrated face validity and good cognitive response properties and may be used independently from the questionnaire.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".