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Record W4361244919 · doi:10.1177/00220426231168082

The Perspective of Young Adults Who Experience Homelessness About the Links Between Music and the Psychoactive Substance Use Trajectory

2023· article· en· W4361244919 on OpenAlexaff
Elise Cournoyer Lemaire, Karine Bertrand, Marie Jauffret‐Roustide, André Lemaître, Christine Loignon

Bibliographic record

VenueJournal of Drug Issues · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsBritish Columbia Centre on Substance UseUniversité de Sherbrooke
Fundersnot available
KeywordsAddictionPsychologyThematic analysisActive listeningHarm reductionPerspective (graphical)HarmTrajectoryPsychological interventionSubstance useDevelopmental psychologyQualitative researchClinical psychologySocial psychologyPsychiatryMedicineSociologyPsychotherapistArtVisual arts

Abstract

fetched live from OpenAlex

This study aimed to describe and understand the links between musical activities (i.e. listening, playing, attending festive events, belonging to music-based communities) and the addictive trajectory of homeless young adults who experience problematic psychoactive substance (PS) use. Semi-structured qualitative interviews were conducted with 15 homeless young adults aged 18 to 30 years old, to explore how music modulated their addictive trajectory. A thematic and trajectory analysis were performed. Music most often constituted a tool used to control, reduce, or recover from problematic PS use, and sometimes led to the initiation of novel substances, increased consumption, and relapses. These benefits and harms varied according to specific individual and contextual factors. Almost half of the sample reported no link between music and PS use. A better comprehension of the links between music and the addictive trajectory will guide the development of adapted harm reduction interventions that account for homeless young adults’ strengths.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.056
GPT teacher head0.398
Teacher spread0.342 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations2
Published2023
Admission routes1
Has abstractyes

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