People with opioid use disorders: A taxonomy of treatment entrants to support the development of a <scp>profile‐based</scp> approach to care
Bibliographic record
Abstract
INTRODUCTION: People with opioid use disorders (OUD) present with high levels of medical and psychosocial vulnerabilities. In recent years, studies have highlighted a shift in demographic and biopsychosocial profiles of people with OUD. In order to support the development of a profile-based approach to care, this study aims to identify different profiles of people with OUD in a sample of patients admitted to a specialised opioid agonist treatment (OAT) facility. METHODS: Twenty-three categorical variables (demographic, clinical, indicators of health and social precariousness) were retrieved from a sample of 296 patient charts in a large Montréal-based OAT facility (2017-2019). Descriptive analyses were followed by a three-step latent class analysis (LCA) to identify different socio-clinical profiles and examine their association with demographic variables. RESULTS: The LCA revealed three socio-clinical profiles: (i) "polysubstance use with psychiatric, physical and social vulnerabilities" (37% of the sample); (ii) "heroin use with vulnerabilities to anxiety and depression" (33%); (iii) "pharmaceutical-type opioid use with vulnerabilities to anxiety, depression and chronic pain" (30%). Class 3 individuals were more likely to be aged 45 years and older. DISCUSSION AND CONCLUSION: While current approaches (such as low- and regular-threshold services) may be suited for many OUD treatment entrants, there may be a need to improve the continuum of care between mental health, chronic pain, and addiction services for those characterised by the use of pharmaceutical-type opioids, chronic pain and older age. Overall, the results support further exploring profile-based approaches to care, tailored to subgroups of patients with differing needs or abilities.
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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 | 0.001 |
| 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".