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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".