MétaCan
Menu
Back to cohort
Record W3112909852 · doi:10.2196/20036

Transdiagnostic Internet Intervention for Indonesian University Students With Depression and Anxiety: Evaluation of Feasibility and Acceptability

2020· article· en· W3112909852 on OpenAlexvenueno aff
Metta Rahmadiana, Eirini Karyotaki, Mieke H. J. Schulte, David Daniel Ebert, Jan Passchier, Pim Cuijpers, Thomas Berger, Wouter van Ballegooijen, Supra Wimbarti, Heleen Riper

Bibliographic record

VenueJMIR Mental Health · 2020
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersLembaga Pengelola Dana Pendidikan
KeywordsAnxietyPsychological interventionIntervention (counseling)UsabilityClinical psychologyDepression (economics)Quality of life (healthcare)MedicineThe InternetPsychologyScale (ratio)PsychiatryNursing

Abstract

fetched live from OpenAlex

BACKGROUND: University students with depression and anxiety do not easily receive or seek treatment; therefore, internet-based interventions have been suggested to be a promising way to improve treatment accessibility and availability. However, it has not been examined whether a guided, culturally adapted, transdiagnostic, internet-based intervention is effective for treating symptoms of depression, anxiety, or both among university students in Indonesia. OBJECTIVE: This study aims to investigate the feasibility (acceptability and satisfaction, usability, and uptake) of a guided, culturally adapted, transdiagnostic, internet-based intervention among university students with symptoms of depression, anxiety, or both in Indonesia. METHODS: Students from Universitas Gadjah Mada, Yogyakarta, Indonesia, were screened for symptoms of depression, anxiety, or both, and filled online informed consent, demographic questionnaires, and a quality of life measure at pretreatment assessment (T0). Subsequently, the participants started the intervention. Seven weeks after T0, the primary outcomes of this feasibility study were analyzed at posttreatment assessment (T1) using the 8-item Client Satisfaction Questionnaire (CSQ-8) and the System Usability Scale (SUS). Mean and SDs for the CSQ-8 and SUS were calculated to examine feasibility. Within-group secondary outcomes (depression, anxiety, and quality of life) were inspected for outliers and normal distribution. Paired-sample t tests were used to investigate differences between time points of secondary outcomes. A mixed-method approach of quantitative and qualitative analyses was adopted. Both the primary and secondary outcomes were additionally explored with an individual semistructured interview and synthesized descriptively. RESULTS: A total of 50 participants completed the intervention. We found a moderate to high level of satisfaction and acceptability, a slightly below-average level of desirable usability (≥70), and an adherence rate of 52% which was higher than expected given the novelty of the intervention. Results for the secondary outcomes indicated a decrease in depression and anxiety. For depression, the overall mean difference between the 2 time points for depression was 3.92 (95% CI 2.75-5.1; Hedges g 1.15; P<.001). For anxiety, the overall mean difference between the 2 time points was 3.34 (95% CI 2.06-4.61; Hedges g 1.02; P<.001). Further, a moderate effect in improving quality of life was found (g=0.50). Overall, participants were positive about the online intervention and ECoaches (online guidance), and they found the intervention to be culturally appropriate. CONCLUSIONS: A culturally adapted, transdiagnostic, internet-based intervention appears to be acceptable and feasible for reducing symptoms of depression, anxiety, or both, and increasing quality of life in university students in Indonesia. Future studies should include a randomized controlled trial to assess the effectiveness of such interventions as they may supplement existing counseling services in universities, reduce the treatment costs, and maximize treatment accessibility in low-resourced settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.invent.2018.11.002.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.063
GPT teacher head0.432
Teacher spread0.369 · 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.

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

Citations47
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Mental HealthSame topicDigital Mental Health InterventionsFrench-language works237,207