A Participatory Evaluation of a First Nations Substance Abuse Treatment Centre
Bibliographic record
Abstract
Research on alcohol and substance use for First Nations populations frequently describes the problem (nature and severity, risk factors), but does not often address intervention and what works well for clients who engage in treatment. This thesis provides a comprehensive participatory evaluation of the six-week residential ‘Namgis Treatment Centre (NTC) program in Alert Bay, British Columbia. Client intake files ( n = 218) were reviewed for clients who participated in 17 different six-week sessions over a two and a half years along with reviewing evaluation forms completed by clients ( n = 131) at the end of treatment. Assessing program theory involved developing a program logic model. Assessing program impact included administering a telephone follow-up survey, developed in conjunction with all of the NTC staff, with clients who had been out of treatment for 3 to 37 months ( n = 91, 52.7% male and 47.3% female participants). In total, 24 clients were abstinent at the time of the interview, and 67 clients (73.6%) had a relapse on average 155.29 ( SD = 167.77) days after completing treatment. Cox regression univariate and bivariate analysis revealed that pre-treatment variables (age, gender, education, history of abuse, presence of medical or psychological issue and involvement in culture) were not associated with time to relapse or what happened after relapse (abstinence again, harm reduction or resuming pre-treatment consumption levels). Post-treatment variables (aftercare plan, support from friends, family, a drug and alcohol counsellor, AA/NA meetings, AA/NA sponsor) were not associated with time to relapse except using the after care plan. However, the aggregate of support items influenced what happened after relapse. The greater number of supports the client had, the more likely they were to be completely abstinent, and the less supports the client had, the more likely they were to completely relapse. NTC staff and community members were consulted with for their input into the interpretation and implication of the findings, and recommendations were shared with key NTC policy makers. Based on the findings in this project, it is apparent that follow-up and aftercare are critical for effective treatment and lasting behaviour change.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".