The Italian cross-cultural adaptation of the Social Vulnerability Index
Bibliographic record
Abstract
Background: Social vulnerability is a key health domain that is associated with frailty and disability in older adults, informing clinical trajectories and outcomes both on an individual and at a population level. The underlying concept is that frailty develops with the accumulation of physical, psychological, and social deficits, and the identification of losses in the social domain may allow for designing tailored interventions in a timely fashion. The aim of the present study was to adapt the Social Vulnerability Index (SVI) to the Italian language and culture for these purposes. Methods: The Italian version of the SVI (SVI-I) has been developed through a comprehensive cross-cultural adaptation of the original Canadian SVI. This process involved four steps: initial translation, synthesis of translations, back translation, and a Delphi procedure. Results: The result of the study is the face-valid 38-item SVI-I. Based on the Delphi procedure, the SVI-I can be administered to Italian-speaking, over-65, community-dwelling individuals not affected by cognitive decline. Conclusion: This study develops the first index to measure social vulnerability in the Italian-speaking population, aiming at a multidimensional approach to address social and healthcare needs. If proven effective in subsequent validation studies, it may enhance geriatric assessments, improve early social vulnerability detection, and support tailored care plans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".