Polysubstance use and lived experience: new insights into what is needed
Bibliographic record
Abstract
PURPOSE OF REVIEW: During the current overdose crisis in the United States and Canada, both polysubstance use and interventions involving people with lived experience of substance use disorder have grown. This review investigates the intersection of these topics to recommend best practices. RECENT FINDINGS: We identified four themes from the recent literature. These are ambivalence about the term lived experience and the practice of using private disclosure to gain rapport or credibility; efficacy of peer participation; promoting equitable participation by fairly compensating staff hired for their lived experience; challenges unique to the current polysubstance-dominated era of the overdose crisis. People with lived experience make important contributions to research and treatment, especially given the additional challenges that polysubstance use creates above and beyond single substance use disorder. The same lived experience that can make someone an excellent peer support worker also often comes with both trauma related to working with people struggling with substance use and lack of opportunities for career advancement. SUMMARY: Policy priorities for clinicians, researchers and organizations should include steps to foster equitable participation, such as recognizing expertise by experience with fair compensation; offering career advancement opportunities; and promoting self-determination in how people describe themselves.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".