Utilization of the Transtheoretical Model to Determine the Qualitative Impact of a Tribal FASD Prevention Program

• Published in: SAGE open 2019-01, Vol.9 (1)
• Main Authors: Lowrey, Olivia, Ciampaglio, Kaitlyn, Messerli, Jamie L., Hanson, Jessica D.
• Format: Article
• Language: English
• Subjects: Alcohol; Behavior; Birth control; Fetal alcohol syndrome
• ISSN: 2158-2440; 2158-2440
• DOI: 10.1177/2158244018822368

Alcohol consumption during pregnancy can lead to damaging effects on an infant’s health, including fetal alcohol spectrum disorders. Project Changing High-risk alcOhol use and Increasing Contraception Effectiveness Study (CHOICES), a program developed to reduce alcohol-exposed pregnancies through decreased alcohol consumption and increased birth control use, has been implemented with success in a variety of populations. The CHOICES program was structured to align with the transtheoretical model (Stages of Change), a popular public health model. Although studies have described the Stages of Change in the context of a variety of health behaviors, none have addressed the qualitatively distinct characteristics of each stage in the context of American Indian (AI) women’s alcohol and birth control use. A framework analysis of 203 participants’ written responses during their experience in the Oglala Sioux Tribe (OST) CHOICES Program was conducted. As a conceptual framework, the transtheoretical model of behavior change was applied to the participants’ experiences, with two staff reading the open-ended responses and coding based on the stage of change. Participants’ responses suggest qualitatively distinct stages as well as a progression through the stages for both behaviors during the course of the program. Many participants mentioned their children, education, and work as inspiration to decrease their unhealthy behaviors. Common barriers to behavior change were found across both behaviors. The open-ended responses uncover common themes in the experiences of the participants. These results can help inform future programs which hope to address the needs of AI communities.