“Give me the reigns of taking care of myself with a home”: Healing environments in an Indigenous-led alcohol harm reduction program
Bibliographic record
Abstract
BACKGROUND: Distinct from western Managed Alcohol Programs (MAPs), Indigenous-led alcohol harm reduction programs can be defined by both 'culture as healing' and decolonized harm reduction philosophies. We sought to explore experiences of Indigenous 'family members' (participants) in an Indigenous-led alcohol harm reduction program and culturally supportive housing to identify appropriate supports according to family member perspectives, and to inform delivery of the program. METHODS: Situated within an Indigenous-western research partnership, we completed semi-structured interviews with seven family members of an Indigenous-led alcohol harm reduction and culturally supportive housing program. Community-guided protocols informed relational knowledge gathering practices including semi-structured in-depth interviews, qualitative thematic analysis, collaborative interpretation of findings, and development of knowledge products. RESULTS: Family members highlighted the importance of tailored Indigenous-led alcohol harm reduction in shifting their relationships to alcohol from survival to having choice and control of their drinking (It's a choice I'm making right now). The provision of varied and incremental culture-based opportunities (Multiple pathways for connecting to culture) facilitated engagement with culture as healing. Policies that honour respect and autonomy were identified as supportive to healing and harm reduction, countering family members' experiences in western spaces (Give me the reigns of taking care of myself with a home). CONCLUSIONS: An Indigenous-led alcohol harm reduction program within a model of culture as healing facilitated shifts in relationships to alcohol, providing a space where family members could explore long term goals of healing and connection to culture. Family members' experiences and recommendations offer key considerations for the design of Indigenous-led harm reduction and culture as healing models. Recommendations emphasize the provision of tailored alcohol harm reduction plans in parallel to multiple and accessible opportunities for connection to culture as healing in order to meet diverse participant goals and relationships to alcohol and culture.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| 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".