Partnership, Knowledge Translation, and Substance Abuse Prevention With a First Nations Community
Bibliographic record
Abstract
BACKGROUND: Having identified substance abuse as an issue of concern in their community, the Alexis Nakota Sioux Nation invited University of Alberta researchers to partner on the cultural adaptation, delivery, and evaluation of a school-based drug and alcohol abuse prevention program. Researchers conducted a literature review of available drug and alcohol prevention programs for children and youth, identifying the Life Skills Training (LST) program as a viable model for cultural adaptation. OBJECTIVES: Four program objectives were developed: (1) Review and cultural adaptation of the elementary and junior high LST programs, (2) delivery of the adapted programs, (3) measurement of changes in students' knowledge of the negative effects of drug and alcohol use, attitudes toward drugs and alcohol, drug and alcohol refusal and life skills, and changes in self-esteem/self-concept, and (4) documentation of the community's experience of the project. METHODS: Using the principles of community-based participatory research (CBPR), we employed both qualitative and quantitative methods to evaluate the impact of the project. RESULTS: Qualitative evaluation of the program adaptation and implementation were both positive. Qualitative measures of program impact on students revealed a positive effect, whereas results of the quantitative measures were mixed. CONCLUSIONS: Culturally adapted, evidence-based programs can have a positive effect on Aboriginal youth and their communities. Strategies to expand knowledge translation (KT) when working with Aboriginal communities include working to create an "ethical space" that draws on the strengths of both Western and Indigenous worldviews.
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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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".