Maternal health disparities linked to stressful life events: a cross-sectional study of industrialized Italian cities
Bibliographic record
Abstract
BACKGROUND: Understanding the impact of family life stressors on maternal health is crucial, particularly in highly industrialized areas. This study assessed the validity of an Italian-language version of the Crisis in Family Systems-Revised (CRISYS-R) survey in Northern and Southern Italian cohorts. METHODS: Mothers (n = 252) completed an Italian version of CRISYS-R, translated from English using the forward-backward method. At least 14 days after initial survey completion, a random subset of mothers (n = 44) retook CRISYS-R. Information about family demographics, socioeconomic status, and maternal health were collected by self-report on structured surveys. Statistical analyses were performed in R. RESULTS: Test-retest analysis yielded a Pearson coefficient of 0.714 (Brescia: 0.845, Taranto: 0.726). Cronbach's alpha coefficient for internal consistency was 0.765 (Brescia: 0.718, Taranto: 0.784). In multivariable regression, the total number of stressors reported on the initial CRISYS-R test was positively associated with: poor maternal mental health (p < 0.001), poor maternal physical health (p < 0.01), and residence in Southern rather than Northern Italy (p = 0.02). Univariate correlations yielded similar results, plus a negative correlation between annual family income and total life stressors (p < 0.05). CONCLUSIONS: Statistical analyses support the validity and reliability of an Italian-language CRISYS-R in industrialized areas, while highlighting relationships between family stress and maternal mental and physical health. This survey instrument has the potential to inform public health policies and interventions serving families in Italian-speaking areas with high burdens of industrial pollution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".