Community trial evaluating the integration of Indigenous healing practices and a harm reduction approach with principles of seeking safety in an Indigenous residential treatment program in northern Ontario
Bibliographic record
Abstract
OBJECTIVE: Our primary objective was to evaluate how the Indigenous Healing and Seeking Safety (IHSS) model impacted residential addiction treatment program completion rates. Our secondary objective was to evaluate health service use 6 months before and 6 months after residential treatment for clients who attended the program before and after implementing IHSS. METHODS: We observed clients of the Benbowopka Residential Treatment before IHSS implementation (from April 2013 to March 31, 2016) and after IHSS implementation (from January 1, 2018 - March 31, 2020). The program data were linked to health administration data, including the Ontario Health Insurance Plan (OHIP) physician billing, the Registered Persons Database (RPDB), the National Ambulatory Care Reporting System (NACRS), and the Discharge Abstract Database (DAD). Chi-square tests were used to compare patient characteristics in the no-IHSS and IHSS groups. We used logistic regression to estimate the association between IHSS and treatment completion. We used generalized estimating equation (GEE) regression model to evaluate health service use (including primary care visits, ED visits overall and for substance use, hospitalizations and mental health visits), Results: There were 266 patients in the no-IHSS group and 136 in the IHSS group. After adjusting for individual characteristics, we observed that IHSS was associated with increased program completion rates (odds ratio = 1.95, 95% CI 1.02-3.70). There was no significant association between IHSS patients' health service use at time one or time two. Primary care visits time 1: aOR 0.55, 95%CI 0.72-1.13, time 2: aOR 1.13, 95%CI 0.79-1.23; ED visits overall time 1: aOR 0.91, 95%CI 0.67-1.23, time 2: aOR 1.06, 95%CI 0.75-1.50; ED visits for substance use time 1: aOR 0.81, 95%CI 0.47-1.39, time 2: aOR 0.79, 95%CI 0.37-1.54; Hospitalizations time 1: aOR 0.78, 95%CI 0.41-1.47, time 2: aOR 0.76, 95%CI 0.32-1.80; Mental health visits time 1: aOR 0.66, 95%CI 0.46-0.96, time 2: aOR 0.92 95%CI 0.7-1.40. CONCLUSIONS: Our results indicate that IHSS positively influenced program completion but had no significant effect on health service use. TRIAL REGISTRATION: This study was registered with clinicaltrials.gov (identifier number NCT04604574). First registration 10/27/2020.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".