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Record W4415277578 · doi:10.17615/y37r-q326

Reconstructing Cross-Cultural Meanings of Addiction Among Women from Three Countries

2025· article· en· W4415277578 on OpenAlexaboutno aff
Caitlyn D. Placek, Sugandh Gupta, Vandana Phadke, Lora Adair, Maninder Shah Singh, Ishita Jain

Bibliographic record

VenueUNC Libraries · 2025
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsAddictionPsychological interventionSubstance useCultural diversityCultural valuesCultural sensitivityFocus groupCultural background

Abstract

fetched live from OpenAlex

The gender gap in drug use is narrowing in regions where access to criminalized substances, such as opioids, is increasing. While research shows that substance use is gendered, less is known about the cultural norms and values shaping women’s drug use, as most studies focus on men. Cross-national comparisons of cultural models of addiction are needed to better understand how addiction is perceived and to inform culturally responsive treatment approaches for women. This study examined cultural models of addiction among reproductive-aged women receiving treatment for substance misuse in London, Toronto, and Delhi. Participants completed a semi-structured questionnaire with open-ended and free-list prompts. Findings revealed shared cultural models attributing drug use to psychological factors, such as self-medicating to manage negative emotions or enhance positive ones, as well as relational, developmental, and biological influences. In conclusion, the study highlights the importance of incorporating cultural models into research and treatment. By using an inductive approach to explore meanings surrounding drug use among people in recovery, researchers can better understand how interventions are received and interpreted through existing internal frameworks.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.373
Teacher spread0.335 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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