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Record W4410085781 · doi:10.2196/73382

Short, Animated Storytelling Video to Reduce Addiction Stigma in 13,500 Participants Across Multiple Countries Through an Online Approach: Protocol for a Randomized Controlled Trial

2025· article· en· W4410085781 on OpenAlexvenueno aff
Maya Adam, Maxwell C. Klapow, Merlin Greuel, Misha Seeff, Julia K. Rohr, Andrew S. Gordon, Doron Amsalem, Till Bärnighausen

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialAddictionPsychologyPsychological interventionEmpathyStigma (botany)MedicineClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Stigma toward people with addiction is a well-documented phenomenon that dramatically impacts help-seeking, treatment, and recovery. Interventions aimed at reducing stigma toward those with addiction must overcome the frequent mischaracterization of addiction as a failure of judgment rather than a chronic, treatable illness. Previous research has demonstrated that social contact with people recovering from addiction can promote empathy and reduce stigma, but social contact is difficult to scale. Short, animated storytelling (SAS) is a novel health communication approach that scales easily because it can leapfrog barriers associated with language, culture, literacy, and education levels. OBJECTIVE: This study will investigate the effect of a cross-culturally accessible SAS video intervention aimed at reducing stigma and increasing empathy toward people with addiction. We also seek to gain insight into the mechanisms of action of this SAS intervention by measuring the contribution of sound design to their effect. METHODS: We will conduct a randomized controlled trial with 13,500 adult participants from the United States, the United Kingdom, and South Africa, recruited online via Prolific Academic and randomized into 3 arms, per country. The 2 intervention arms will receive a wordless, social contact-based SAS video, one arm with a soundtrack and one without. The third arm will receive an educational video about addiction. Validated questionnaires will be used to assess our primary outcome, addiction stigma, and secondary outcomes, optimism, warmth toward the subject, and hopefulness, at baseline, immediately post exposure, and 2 weeks later. Ethics clearance was obtained on August 15, 2024, from the Stanford University institutional review board (protocol 76457). RESULTS: This trial was funded in January 2025 by the Heidelberg Institute of Global Health, the Faculty of Medicine at Heidelberg University, in Germany. As of March 2025, no data have been collected. The estimated start date for this trial is May 15, 2025. We expect to complete data collection by July 1, 2025, and expect results to be published in the spring of 2026. CONCLUSIONS: Here, we present the protocol for an online, multicountry, randomized controlled trial. This trial is designed to measure the effect of an innovative approach to global health communication (wordless, short, and animated storytelling) on addiction stigma in 3 global regions. These findings will inform the design of future scalable, digital health storytelling interventions for global audiences while exploring the capacity of SAS to shift public health attitudes and perceptions. Furthermore, if effective, the intervention described here could be disseminated broadly via social media and other online platforms. TRIAL REGISTRATION: ClinicalTrials.gov NCT06705205; https://clinicaltrials.gov/study/NCT06705205. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/73382.

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)0.021
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.062
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.021
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0090.005
Bibliometrics0.0030.003
Science and technology studies0.0040.003
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0620.008

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 head0.696
GPT teacher head0.673
Teacher spread0.023 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations3
Published2025
Admission routes1
Has abstractyes

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