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Record W4407241385 · doi:10.2196/65478

Video- Versus Text-Based Psychoeducation in Web-Based E-Mental Health Programs: Randomized Controlled Trial

2025· article· en· W4407241385 on OpenAlexvenueno aff
Swantje Borsutzky, Josefine Gehlenborg, Lara Rolvien, Steffen Moritz

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychoeducationPreprintMental healthTelepsychiatryRandomized controlled trialPsychologyWorld Wide WebMedicineComputer sciencePsychotherapistPsychiatryTelemedicinePsychological interventionPolitical scienceHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: Mental health disorders affect 1 in 8 people worldwide, yet many face barriers to accessing care. E-mental health interventions, including self-guided internet-based programs, offer promising solutions. However, the mechanisms driving knowledge gain in such programs remain poorly understood. The role of medium, topic, sequence, and confidence and their interaction in learning outcomes need further investigation. Additionally, the influence of knowledge gaps on the outcome of psychoeducational intervention is not well understood (eg, whether psychoeducation requires an existing knowledge gap to be effective). OBJECTIVE: This randomized controlled trial investigated the role of medium, topic, sequence, and participants' initial knowledge levels on knowledge gain and confidence in fully automated self-guided e-mental health psychoeducation. METHODS: A total of 158 adults (mean age 34, SD 12.4 years; n=118, 74.7% female) were randomized to 8 experimental conditions (receiving video, texts, or both containing psychoeducational content on sleep or social competence; n=142) or a control group (neutral video; n=16). The fully automated interventions (videos) were developed for use in web-based e-mental health interventions. They address transdiagnostic symptoms and hence are relevant across various disorders. To assess the added value of video production for knowledge gain, text-based scripts corresponding to the video content were created and compared. All interventions and outcome assessments were delivered on the web via Qualtrics without face-to-face components. Pre- and postintervention knowledge was assessed using a validated 30-item knowledge test (true/false). Confidence in responses was rated on a 0% to 100% scale. Statistical analyses included 3-way ANOVA and multivariate ANOVA. RESULTS: =2.43; P=.02; d=0.20). No significant differences in knowledge gain were found between video and text formats. Confidence in correct answers increased significantly in the experimental group (mean 42.82, 95% CI 41.15-44.50 to mean 51.67, 95% CI 49.28-54.04), with larger gains for social competence than sleep. Confidence in the control group remained unchanged. CONCLUSIONS: Both video and text formats effectively facilitated knowledge gain in e-mental health interventions, with no clear advantage of one medium over the other. Participants with prior deficits learned more in areas where they initially lacked knowledge. Confidence in correct answers increased alongside knowledge, highlighting psychoeducation's role in promoting self-efficacy. Future research should explore multimedia integration to enhance adherence and symptom improvement. TRIAL REGISTRATION: German Clinical Trials Register DRKS00026722; https://drks.de/search/en/trial/DRKS00026722.

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)0.005
metaresearch head score (Gemma)0.009
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0080.004
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0210.002

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 head0.088
GPT teacher head0.541
Teacher spread0.453 · 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
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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