Development and Psychometric Assessment of a Questionnaire for the Detection of Invisible Violence against Women.

BACKGROUND: Invisible violence against women (IVAW) can be understood as the set of attitudes, behaviors, and subtle beliefs that men use to subordinate women and that are culturally accepted. These behaviors can be a risk factor for intimate partner violence (IPV), so it is important to design tools that allow us to detect it early. The aim of this study was to design and psychometrically assess a questionnaire for the detection of invisible violence against women (Q-IVAW). METHODOLOGY: A descriptive cross-sectional methodological study carried out in three phases: (1) development of the initial version; (2) pilot study (N = 51); and (3) final validation study (N = 990). The tool's reliability, validity, and legibility were assessed. To assess reliability, the internal consistency (Cronbach's α) was analyzed. The validity assessment included an analysis of content, criterion, and construct validity. RESULTS: The EFA revealed that the Q-IVAW was comprised of five factors that explained 55.85% of the total variance found. The Q-IVAW showed very high reliability (α = 0.937), excellent content validity, and good construct validity. The criterion validity analysis showed a moderate correlation between A-IPVAW and Q-IVAW (r = 0.30; p < 0.001). CONCLUSION: The psychometric assessment of the Q-IVAW yielded good results, which could support the tool's ability to assess how often women are subjected to inviable violent behaviors by their partners.

Factors That Determine the Dietary Diversity Score in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador.

Inadequate food and nutrition affect human well-being, particularly for many poor subpopulations living in rural areas. The purpose of this research was to analyze the factors that determine the Household Dietary Diversity Score (HDDS) in the rural area of the Paute River Basin, Azuay Province, Ecuador. The sample size of 383 surveys was determined by a stratified random sampling method with proportional affixation. Dietary diversity was measured through the HDDS, with 12 food groups (cereals; roots and tubers; fruits; sugar/honey; meat and eggs; legumes or grains; vegetables; oils/fats; milk and dairy products; meats; miscellaneous; fish and shellfish) over a recall period of 7 days. A Poisson regression model was used to determine the relationship between the HDDS and sociodemographic variables. The results show that the average HDDS of food consumption is 10.89 foods. Of the analyzed food groups, the most consumed are cereals; roots and tubers; fruits; sugar/honey. In addition, the determinants that best explain the HDDS in the predictive model were housing size, household size, per capita food expenditure, area of cultivated land, level of education, and marital status of the head of household. The tools used in this research can be used to analyze food and nutrition security interventions. Furthermore, the results allow policymakers to identify applicable public policies in the fight against hunger.