Turtle Finding Fact Sheet: The Role of the Treatment Provider in Aboriginal Women's Healing from Illicit Drug Abuse.
Bibliographic record
Abstract
Our research identifies key skills and traits for service providers working with Aboriginal women that assists them with re-claiming their cultural identity. The "Turtle Finding Fact Sheet: The Role of the Treatment Provider in Aboriginal Women's Healing from Illicit Drug Abuse" was created to disseminate and commence discussion on this initial finding from our community-based research project in Canada. The study overall focussed on the role of identity and stigma in the healing journeys of criminalized Aboriginal women from illicit drug abuse. Our team is committed to sharing its finding with the community from which the information was collected-workers in the National Native Alcohol and Drug Abuse Program (NNADAP). The Fact Sheet is based on a sample of interviews with substance abuse treatment providers, and was verified with women in treatment and who have completed treatment. In recent years, the addictions literature has increased its attention toward the importance of the therapeutic alliance between treatment providers and clients(1), although understanding specific to Aboriginal women remains limited. Identity reclamation is central to women's healing journeys and treatment providers have an influential role. This finding is framed in the fact sheet within the cultural understanding of the Seven Teachings of the Grandfathers(2). The fact sheet (8.5x11) has been distributed to the over 700 NNADAP workers, and is also available at no cost in two poster size formats. It is appropriate for anyone providing services to Aboriginal women requiring addictions treatment.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".