Healing the community to heal the individual
Bibliographic record
Abstract
OBJECTIVE To understand the development of culturally based and community-based alcohol and substance abuse treatment programs for aboriginal patients in an international context. SOURCES OF INFORMATION MEDLINE, HealthSTAR, and PsycINFO databases and government documents were searched from 1975 to 2007. MeSH headings included the following: Indians, North American, Pacific ancestry group, aboriginal, substance-related disorders, alcoholism, addictive behaviour, community health service, and indigenous health . The search produced 150 articles, 34 of which were relevant; most of the literature comprised opinion pieces and program descriptions (level III evidence). MAIN MESSAGE Substance abuse in some aboriginal communities is a complex problem requiring culturally appropriate, multidimensional approaches. One promising perspective supports community-based programs or community mobile treatment. These programs ideally cover prevention, harm reduction, treatment, and aftercare. They often eliminate the need for people to leave their remote communities. They become focuses of community development, as the communities become the treatment facilities. Success requires solutions developed within communities, strong community interest and engagement, leadership, and sustainable funding. CONCLUSION Community-based addictions programs are appropriate alternatives to treatment at distant residential addictions facilities. The key components of success appear to be strong leadership in this area; strong community-member engagement; funding for programming and organizing; and the ability to develop infrastructure for long-term program sustainability. Programs require increased documentation of their inroads in this developing field.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".