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Record W4376643011 · doi:10.1097/yco.0000000000000876

Polysubstance use and lived experience: new insights into what is needed

2023· review· en· W4376643011 on OpenAlexaboutno aff
Chelsea L. Shover, Jordan Spoliansky, Morgan Godvin

Bibliographic record

VenueCurrent Opinion in Psychiatry · 2023
Typereview
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
FundersNational Institute on Drug Abuse
KeywordsPolysubstance dependenceLived experiencePsychological interventionCredibilityPsychologyAmbivalencePublic relationsSubstance useMedicineSocial psychologyPolitical sciencePsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.162
GPT teacher head0.437
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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