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Record W4288085783 · doi:10.1177/11782218221103581

Problematic Opioid Use: A Scoping Literature Review of Profiles

2022· article· en· W4288085783 on OpenAlexaff
Léonie Archambault, Karine Bertrand, Michel Perreault

Bibliographic record

VenueSubstance Abuse Research and Treatment · 2022
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsMcGill UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsPsychological interventionPsychologyOpioidMedicineApplied psychologyPsychiatry

Abstract

fetched live from OpenAlex

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.380
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.064
GPT teacher head0.371
Teacher spread0.307 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations4
Published2022
Admission routes1
Has abstractyes

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