Recommendations to measure wellbeing in the workplace. A meta-analysis of the wellbeing measures in the public and private sector
Bibliographic record
Abstract
Recommendations to measure wellbeing in the workplace. A meta-analysis of the wellbeing measures in the public and private sectorWith workforce and culture receiving more attention, private and public organisations are implementing new policies and practices to improve overall staff wellbeing.This paper explores definitions and measures of wellbeing, and compares the application of such measures across public and private organisations.Wellbeing is difficult to define, with several definitions and components being proposed, which makes it even more challenging to measure (Dodge et al. 2012).Accordingly, there is no consensus on how to measure wellbeing which complicates its utility, and blurs perspectives on its antecedents and consequences.The hedonic perspective defines wellbeing as life satisfaction, focusing on happiness and positive affect.The main measure to capture this is the Subjective Happiness Scale (Lyubomirsky & Lepper 1999), and this has been used in many private sector studies (Ashleigh, Higgs & Dulewicz 2012;Edgar et al. 2015).The eudaimonic perspective emphases the fulfilment of finding meaning, such as in achieving the personal career goals.One such measure is the Questionnaire for Eudaimonic Well-being (Waterman et al. 2010).Other studies define wellbeing as psychological safety with measures such as the Team Psychological Safety scale developed by Edmondson (1999).Brunetto, Farr-Wharton and Shacklock (2011) developed the Employee Psychological Wellbeing scale that includes both the eudaimonic and the hedonic components, and has been applied to the public sectors of Australia, New Zealand, Canada and the UK.Some thirty measures have been developed over the past 50 years using different definitions and applied to the organisational context.Taking all of this into account the paper makes recommendations for measuring wellbeing in public and private organisations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".