Sociodemographic Profiles and Clinical Outcomes for Clients on Methadone Maintenance Treatment in a Western Canadian Clinic: Implications for Practice
Bibliographic record
Abstract
INTRODUCTION: Clients on methadone maintenance treatment (MMT) have high attrition rates that are attributed to personal and system-related factors. To develop supportive interventions for these clients, it is imperative to understand social demographic characteristics and challenges that clients in the MMT program face. OBJECTIVES: This article aims to describe (a) the sociodemographic characteristics and clinical profiles of clients in a MMT program, (b) factors that impact their positive clinical outcomes, and (c) the study's implications for practice. METHODS: A retrospective review of 101 randomly selected electronic medical records representing one third of all the records were examined for sociodemographic characteristics, clinical profiles, and outcomes. Descriptive statistics were used to analyze these variables. Interviews with 18 healthcare providers focusing on their experiences of caring for clients in the MMT program were analyzed thematically. RESULTS: The average age of clients on MMT is 35.5 years. Clients had early exposure to alcohol and drugs, and at the time of enrollment to the program, they presented with complex healthcare needs, borne from chronic use, and exposure to adverse traumatic events. Personal and systemic factors impact clients' recovery. These include poverty, homelessness, and inadequate healthcare services. Understanding sociodemographic characteristics, clinical profiles, and clients' challenges is central to the development of supportive interventions that enhance retention to care and recovery.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 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) | 0.002 | 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".