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Record W4408865371 · doi:10.2196/57468

Stigma and Behavior Change Techniques in Substance Use Recovery: Qualitative Study of Social Media Narratives

2025· article· en· W4408865371 on OpenAlexvenueno aff
Annie Chen, Lexie Chenyue Wang, Shana Johnny, Sharon Wong, Rahul K. Chaliparambil, Mike Conway, Joseph E. Glass

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersNational Institute on Drug Abuse
KeywordsSubstance usePsychological interventionPsychologyStigma (botany)Social mediaCannabisIntervention (counseling)Behaviour changeQualitative researchContent analysisNarrativeBehavior changeAddictionSocial stigmaSocial psychologyClinical psychologyMedicinePsychotherapistPsychiatryComputer scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Existing literature shows that persons with substance use disorder (SUD) experience different stages of readiness to reduce or abstain from substance use, and tailoring intervention change strategies to these stages may facilitate recovery. Moreover, stigma may serve as a barrier to recovery by preventing persons with SUDs from seeking treatment. In recent years, the behavior change technique (BCT) taxonomy has increasingly become useful for identifying potential efficacious intervention components; however, prior literature has not addressed the extent to which these techniques may naturally be used to recover from substance use, and knowledge of this may be useful in the design of future interventions. OBJECTIVE: We take a three-step approach to identifying strategies to facilitate substance use recovery: (1) characterizing the extent to which stages of change are expressed in social media data, (2) identifying BCTs used by persons at different stages of change, and (3) exploring the role that stigma plays in recovery journeys. METHODS: We collected discussion posts from Reddit, a popular social networking site, and identified subreddits or discussion forums about 3 substances (alcohol, cannabis, and opioids). We then performed qualitative data analysis using a hybrid inductive-deductive method to identify the stages of change in social media authors' recovery journeys, the techniques that social media content authors used as they sought to quit substance use, and the role that stigma played in social media authors' recovery journeys. RESULTS: We examined 748 posts pertaining to 3 substances: alcohol (n=316, 42.2%), cannabis (n=335, 44.8%), and opioids (n=135, 18%). Social media content representing the different stages of change was observed, with the majority (472/748, 63.1%) of narratives representing the action stage. In total, 11 categories of BCTs were identified. There were similarities in BCT use across precontemplation, contemplation, and preparation stages, with social support seeking and awareness of natural consequences being the most common. As people sought to quit or reduce their use of substances (action stage), we observed a variety of BCTs, such as the repetition and substitution of healthful behaviors and monitoring and receiving feedback on their own behavior. In the maintenance stage, reports of diverse BCTs continue to be frequent, but offers of social support also become more common than in previous stages. Stigma was present throughout all stages. We present 5 major themes pertaining to the manifestation of stigma. CONCLUSIONS: Patterns of BCT use and stigmatizing experiences are frequently discussed in social media, which can be leveraged to better understand the natural course of recovery from SUD and how interventions might facilitate recovery from substance use. It may be important to incorporate stigma reduction across all stages of the recovery journey.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0100.013
Scholarly communication0.0050.007
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.195
GPT teacher head0.500
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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