Developing Community Resilience through Grassroot Initiatives: Comparing Culturally Adapted Substance Use Prevention Programs Directed towards Indigenous Youth in Canada
Bibliographic record
Abstract
Considering the growing prevalence of substance use amongst young people, prevention programs targeting children and adolescents are needed to protect against related cognitive, psychological, and behavioural issues. Preventative programs that have been adapted to Canadian Indigenous cultures in school and family settings are discussed. The first and second phase of the Life Skills Training (LST) program and the Maskwacis Life Skills Training (MLST) program are reviewed, as well as Bii-Zin-Da-De-Da (BZDDD; “Listening to One Another”) and a culturally sensitive smoking prevention program. Motivating factors, comorbid disorders, and at-risk personality types associated with substance use amongst Canadian children and adolescents, specifically Indigenous youth, are considered through the application of the biopsychosocial model. This paper aims to describe the requital efforts being made in Canada towards Indigenous communities, to compare substance use prevention programs targeting Indigenous children and adolescents, and to provide suggestions for future research on preventative interventions directed towards substance use within minority groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".