Evaluation of an inpatient medical withdrawal program in rural Ontario: a 1-year prospective study.
Bibliographic record
Abstract
INTRODUCTION: We present a 1-year program evaluation of the Medical Withdrawal Support Service (MWSS) provided at the Sioux Lookout Meno Ya Win Health Centre. The centre's service area includes 4 rural municipalities and 28 First Nations communities. The program involves inpatient detoxification for opioid dependence with the use of buprenorphine-naloxone. METHODS: Data were collected from preadmission interviews (i.e., medical history, substance use history, previous counselling, social history, previous addiction treatment and screening tools used during the interview); discharge forms (i.e., length of stay, maximum dose of prescribed buprenorphine-naloxone and client goals); and postdischarge interviews. RESULTS: Overall, 81% of the clients successfully completed the program. Two weeks after discharge, 48% of clients reported continued abstinence. At 3-month follow-up, 32% were abstinent, and at 6 months, 30% were abstinent. CONCLUSION: The MWSS shows positive outcomes for many clients, their families and communities. Clients returned to work and school, became more engaged in healthy meal preparation and exercise, spent more time with family and were more involved as leaders in their communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".