Formative Research and Cultural Tailoring of a Substance Abuse Prevention Program for American Indian Youth: Findings From the Intertribal Talking Circle Intervention
Bibliographic record
Abstract
Background. Substance use among American Indians (AIs) is a critical health issue and accounts for many health problems such as chronic liver disease, cirrhosis, behavioral health conditions, homicide, suicide, and motor vehicle accidents. In 2013, the highest rates of substance use and dependence were seen among AIs when compared to all other population groups, although these rates vary across different tribes. Among AI adolescents, high rates of substance use have been associated with environmental and historical factors, including poverty, historical trauma, bicultural stress, and changing tribal/familial roles. Our project, the Intertribal Talking Circle intervention, involved adapting, tailoring, implementing, and evaluating an existing intervention for AI youth of three tribal communities in the United States. Formative Results. Community partnership committees (CPCs) identified alcohol, marijuana, and prescription medications as high priority substances. CPC concerns focused on the increasing substance use in their communities and the corresponding negative impacts on families, stating a lack of coping skills, positive role models, and hope for the future as concerns for youth. Cultural Tailoring Process Results. Each site formed a CPC that culturally tailored the intervention for their tribal community. This included translating Keetoowah-Cherokee language, cultural practices, and symbolism into the local tribal customs for relevance. The CPCs were essential for incorporating local context and perceived concerns around AI adolescent substance use. These results may be helpful to other tribal communities developing/implementing substance use prevention interventions for AI youth. It is critical that Indigenous cultures and local context be factored into such programs.
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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 | 0.031 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".