Development and Validation of the Pregnancy Guilt Assessment Scale (PGAS): A Specific Tool for Assessing Guilt in Pregnancy

Abstract

Background: Gestational guilt is an understudied emotional experience that can affect maternal well-being and prenatal bonding. This study aimed to develop and validate the Pregnancy Guilt Assessment Scale (PGAS) in a sample of Spanish pregnant women, assessing its factorial structure, reliability, and validity. Methods: Four phases were conducted: (1) item generation through focus groups (n = 17) and cognitive interviews (n = 8); (2) expert content validation (n = 3); (3) exploratory factor analysis (EFA) in a pilot sample (n = 85); and (4) confirmatory factor analysis (CFA) and validity testing in an independent sample (n = 171). Additional measures included antenatal depression, prenatal distress, affect, self-esteem, social support, and dispositional guilt. Internal consistency, correlations, and multiple regressions assessed reliability and convergent and incremental validity. Results: The final 16-item PGAS showed a bifactor structure with a general factor and four dimensions: G-LSC (Lack of self-care), G-UEE (Unmet emotional expectations), G-SP (Social pressure), and G-CWR (Conflict with work role). The model demonstrated good fit (χ2 = 109.42, df = 88, p = 0.061; CFI = 0.974; TLI = 0.965; RMSEA = 0.069; SRMR = 0.030) and high reliability (α total = 0.96; ω = 0.98; subscales α = 0.90–0.94). PGAS scores correlated positively with dispositional guilt, negative affect, prenatal distress, and antenatal depression. In regressions, G-UEE uniquely predicted depression (β = 0.213, p = 0.002) and G-SP predicted distress (β = 0.303, p < 0.001). Women who had considered pregnancy termination scored higher on guilt (p < 0.001). Conclusions: The PGAS is a reliable and valid instrument for assessing pregnancy-related guilt in Spanish women, with potential relevance for perinatal mental health research and clinical practice, while future studies should evaluate its performance in other cultural settings.