Background: Contraception is important for reproductive autonomy, yet many Latinas do not use contraception consistently despite research reporting a desire to do so. Factors varying in priority and value come into play during contraceptive decision making. When measuring these, relevant survey items may vary by populations.
Aim: This study focused on developing an ethnically responsive, patient-centered, content-valid survey for measuring factors that influence contraceptive decision making among immigrant Latinas.
Method: Nonpregnant self-identified Latinas ages 15 to 24 years in Baltimore, MD, were recruited from a family planning facility. Using the theory of planned behavior as a theoretical framework and prior formative research, initial survey items were drafted (Step 1). Content validation and cognitive interviewing procedures (Step 2 and Step 3) were used to develop final items.
Results: Final items (27) were content-validated by the target population; items reflect important factors and relevant contexts affecting contraceptive decision making among Latinas in Baltimore.
Discussion: These theory-based items provide an important contribution to the literature because they measure and explore factors related to contraceptive decision making in an understudied population. Providers might consider these factors during counseling to build patient-centered communication. These items might serve to measure responses to theory of planned behavior-based interventions designed to improve the contraceptive counseling of Latinas.
Background/objective: Hispanics are about 1.5 times as likely as non-Hispanic Whites to experience Alzheimer's disease and related dementias (AD/ADRD). Eight percent of AD/ADRD caregivers are Hispanics. The purpose of this article is to provide a methodological case study of using data mining methods and the Twitter platform to inform online self-management and social support intervention design and evaluation for Hispanic AD/ADRD caregivers. It will enable other researchers to replicate the methods for their phenomena of interest.
Method: We extracted an analytic corpus of 317,658 English and Spanish tweets, applied content mining (topic models) and network structure analysis (macro-, meso-, and micro-levels) methods, and created visualizations of results.
Results: The topic models showed differences in content between English and Spanish tweet corpora and between years analyzed. Our methods detected significant structural changes between years including increases in network size and subgroups, decrease in proportion of isolates, and increase in proportion of triads of the balanced communication type.
Discussion/conclusion: Each analysis revealed key lessons that informed the design and/or evaluation of online self-management and social support interventions for Hispanic AD/ADRD caregivers. These lessons are relevant to others wishing to use Twitter to characterize a particular phenomenon or as an intervention platform.
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