Problematic Opioid Use: A Scoping Literature Review of Profiles
Bibliographic record
Abstract
Background and Objectives: Problematic opioid use can be defined as opioid use behaviors leading to social, medical, or psychological consequences. In some instances, people presenting problematic opioid use can also meet criteria for an opioid use disorder. A growing body of literature highlights different types of people who use opioids, with contrasting characteristics and initiation patterns. In recent years, dynamic trends in opioid use have been documented and studies have demonstrated a shift in profiles. Methods: A scoping literature review was conducted to identify profiles of people presenting problematic opioid use, in order to support the development of tailored interventions and services. Results: Nine articles met the inclusion criteria. Five classifications emerge from the literature reviewed to distinguish types of people presenting problematic opioid use, according to: (1) the type of opioids used, (2) the route of opioid administration, (3) the level of quality of life, (4) patterns of other drugs used, and (5) dependence severity. While samples, concepts, and measurement tools vary between studies, the most salient finding might be the distinct profile of people presenting problematic use of pharmaceutical-type opioids. Discussion and Conclusions: This scoping review highlights that few studies address distinctive profiles of people presenting problematic opioid use. Geographical and chronological differences suggest that local timely assessments may be needed to tailor the service offer to specific needs. Scientific Significance: Future studies should focus on providing a deep understanding of distinct experiential perspectives and service needs, through exploratory quantitative and qualitative designs.
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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 | 0.022 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.046 | 0.038 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".