Factor Structure, Psychometric Properties, and Measurement Invariance of the Pandemic Experiences and Perceptions Scale Among Italian Hospital Workers
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic represented substantial risks to hospital workers' physical and mental health. The availability of validated measures on the impact of the pandemic on workplaces is crucial for developing data-driven interventions. The primary purpose of our study was to translate it into Italian and assess factor structure, psychometric properties, and measurement invariance of the Pandemic Experiences and Perceptions Scale (PEPS). METHODS: The survey was completed by 766 workers from an Italian hospital. We examined the internal structure of the PEPS using confirmatory factor analyses (CFA) and exploratory structural equation modeling (ESEM) techniques and testing the invariance for clinical vs. nonclinical workers. RESULTS: The six-factor ESEM solution showed an excellent fit to the data (CFI=0.956, TLI=0.932, RMSEA=0.050), supporting the superiority of the ESEM solution. The factorial invariance of the PEPS across occupational roles (clinical vs. nonclinical hospital workers) was supported, and the ESEM-based McDonald's omega was good for all factors. CONCLUSIONS: The results from this study provided evidence for the factorial validity, reliability, and measurement invariance across occupational roles of the Italian version of the PEPS. Thus, the Italian version of the PEPS is a reliable and valid tool for assessing pandemic experiences and perceptions among Italian workers.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".