Prevention of Alcohol Exposed Pregnancies and Fetal Alcohol Spectrum Disorders and Conceptualizing the Adaptation Process for Health Promotion Programs in Urban American Indian/Alaska Native Communities
Bibliographic record
Abstract
Background: This dissertation project builds on an existing partnership and alcohol exposed pregnancies (AEPs) and Fetal Alcohol Spectrum Disorders (FASD) prevention project between the Tucson Indian Center (TIC) and Mel and Enid Zuckerman College of Public Health (MEZCOPH). The TIC provides social services, health and wellness services, and health fairs and cultural celebrations for urban American Indian and Alaska Natives (AI/AN). Reducing the risk of AEPs and FASD among American Indian, Alaska Natives and Aboriginal People of Canada (AI/AN/APC) is a public health priority. AI/AN/APC women report higher rates of alcohol consumption during pregnancy, and AI/AN infants have high rates of FASDs. Effects of AEPs and FASDs can include physical, mental, behavioral, and learning disabilities with possible lifelong implications. This dissertation's overall objective is to document the broad and local approaches employed to adapt health promotion and prevention programs for urban AI/AN populations, with an emphasis on AEPs and FASD. Methods: This dissertation is composed of three parts: 1) a scoping review to explore the representation of adapted AEPs and FASD programs in AI/AN/APC literature; 2) an examination of factors associated with AEPs in an urban AI/AN women; and 3) utilizing a consensus panel and follow-up interviews to define adaptation and document the TICs adaptation process. Result: The scoping review yielded a total of 1,287 peer-reviewed articles. After a full-text review 15 articles met the inclusion criteria and were selected for a full article review. The grey literature search identified 11 AEP and FASD programs not published in the peer-reviewed literature. Data were analyzed on a sample of 119 women urban AI/AN women. Overall, the prevalence rates of factors associated with AEPs among the sample was were relatively low. The consensus panel defined adaptation, documented their adaptation process, identified best practices for adapting health promotion programs, and lessons learned from working with TIC. Conclusions: This dissertation identified a gap in the literature about AEPs and FASD programs among AI/AN/APC, the prevention programs used to address these issues and the perspective of how those who administer culturally relevant programs adapt these programs the meet the needs of AI/AN/APC communities. Data sources that provide information about the health status of urban AI/AN is lacking. This dissertation also identified the prevalence rates of factors that are associated with AEPs among a specific urban AI /AN community. Lastly, this dissertation documented the program adaptation process of a specific AI/AN-serving organization. These findings advance intervention science to understand how prevention programs are adapted for an urban, multitribal AI/AN population.
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".