Measuring Italian Resilience
Bibliographic record
Abstract
Abstract: The Essential Resilience Scale (ERS) measures global trait resilience and three factors of physical, emotional, and social resilience. This study developed an Italian adaptation of the ERS and recruited participants from Italy ( N = 500) to complete the measure along with criterion validity measures of broad personality traits and related psychological concepts. Confirmatory factor analysis (CFA) demonstrated robust evidence for a well-fitting three-factor model of the ERS, with items strongly loading onto their respective latent factors. Utilizing Samejima’s graded response model, most item discrimination values were moderate-to-high, and category threshold parameters were well-distributed throughout the latent continuum. The ERS showed correlations in the expected directions with extraversion, emotionality, optimism, mastery, resilience, behavioral activation, behavioral inhibition, stress, and well-being. Cultural invariance was supported (at the scale- and item-level) with multigroup CFA and differential item functioning (DIF) with a sample of Canadian English speakers ( N = 874). Findings evinced the internal consistency (i.e., total MacDonald’s ω), factorial validity (i.e., three-factor CFA), criterion validity (i.e., personality, temperament), and convergent validity (i.e., trait resilience and well-being) of the Italian ERS. Results suggest the Italian ERS can be applied for measuring resilience for future research studies in Italian-speaking populations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".