The PERMA + 4 Short Scale: A Cross-Cultural Empirical Validation Using Item Response Theory
Bibliographic record
Abstract
Abstract A substantial body of empirical research has used the positive emotions, engagement, relationships, meaning, accomplishment (PERMA) framework to measure building blocks of well-being across diverse samples and cultures, with most studies using the 23-item PERMA-Profiler (Butler & Kern, 2016) or a workplace variant. Donaldson and Donaldson (2021a) added four additional domains (physical health, mindset, environment, economic security; PERMA + 4). Psychometric development and testing of the original, translated, and variant versions of the measure have relied on Classical Test Theory approaches, such as factor analytic methods. In the workplace, valid, brief measures are critical. The current study used item response theory to analyze data from a large sample of Canadian (n = 1,003) and Australian (n = 942) employees to create a 9-item short scale of PERMA + 4. A graded response model showed good item discrimination (a > 1.40), and similar test information compared to the full measure. A short scale of PERMA + 4 will be useful for future studies of the building blocks of well-being and positive functioning, as well as for evaluating well-being programs and interventions within the workplace.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".