Validation of an English version of Index of Psychological Well-being at Work and an English version of the Satisfaction with Work Scale in a multinational population
Bibliographic record
Abstract
The aim of the study is to evaluate the validity and psychometric properties of the English version of the Index of Psychological Well-being at Work scale (IPWBW; Dagenais-Desmarais & Savoie, 2011) and the English version of the Satisfaction with Work Scale (SWWS; Mérino et al, 2021) in a multinational sample. The IPWBW was developed and validated in French with a Canadian sample in 2011. The IPWBW measures various aspects of psychological well-being at work, such as interpersonal fit at work, thriving at work, feeling of competence, perceived recognition of work, and desire to get involved with work. Over the last decade there were a couple of other attempts to use the scale in other countries (e.g., India and Gabon) and languages (e.g, English) (Medzo-M’Engone, 2021; Sandilya & Shahnawaz, 2018). However, there has been no validation study of the IPWBW in English in the UK, US, or in an English-speaking multinational sample. Similarly, the SWWS was developed and validated in Spanish with a Spanish sample in 2021 (Mérino et al, 2021). Furthermore, Portuguese adaptations that included minor changes in the phrasing of select items were also administered to Portuguese-speaking populations (Caycho-Rodríguez et al., 2020; Neto & Fonseca, 2018). Although researchers made direct and reverse translations to develop an English version of SWWS, a validation has not been made in this language. Consequently, the aim of the present study is to examine the psychometric properties of the English versions of the IPWBW and the SWWS, including their internal consistency reliability and factor structure, in an English-speaking multinational sample. We also aim to assess the convergent validity of both instruments with the Daniel’s five-factor measure of affective well-being (D-FAW; Daniels, 2000; Russell & Daniels, 2018) which was developed and validated originally in English across various geographical locations.
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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 | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".