The Role of Gender in Factors Associated With Addiction Treatment Satisfaction Among Long-Term Opioid Users
Bibliographic record
Abstract
OBJECTIVES: To identify factors associated with Opioid Agonist Treatment (OAT) satisfaction and to determine whether these relationships are gender specific. METHODS: This study was based on data collected in a cross-sectional study among long-term opioid-dependent individuals (n = 160; 46.3% women). Participants completed the Client Satisfaction Questionnaire in reference to OAT episodes. Sociodemographic, illicit substance use, health, and addiction treatment history data were collected. Multivariable linear regression was used to determine the relationship between these variables and treatment satisfaction. To explore the potential role of gender in these identified relationships stratified multivariable models were tested. Additional open-ended questions regarding positive and negative perceptions of treatment were collected, and a thematic analysis was conducted. RESULTS: In the multivariable linear regression model, participants who were older, of Aboriginal ancestry, and currently receiving OAT had higher OAT satisfaction scores, whereas participants who had methadone dose preferences of 30 mg or less had lower OAT satisfaction. In stratified analyses among women, the relationship between preferred methadone dose and current OAT remained significantly associated with satisfaction. Open-ended positive and negative perceptions complemented and provided further valuable data to interpret these identified relationships. CONCLUSIONS: To our knowledge, this is the first study to explore the potential role of gender in factors associated with OAT satisfaction. These findings provide valuable information to health care providers working in OAT settings regarding how to address women and men's OAT needs and improve treatment satisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".