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Record W2190289261 · doi:10.2196/resprot.4783

The Feasibility, Acceptability, and Efficacy of Delivering Internet-Based Self-Help and Guided Self-Help Interventions for Generalized Anxiety Disorder to Indian University Students: Design of a Randomized Controlled Trial

2015· article· en· W2190289261 on OpenAlexvenueno aff
Nitya Kanuri, Michelle G. Newman, Josef I. Ruzek, Eric Kuhn, M. Manjula, Megan Jones, Neil Thomas, Jo-Anne Abbott, Smita Sharma, C. Barr Taylor

Bibliographic record

VenueJMIR Research Protocols · 2015
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersSchool of Medicine, Stanford University
KeywordsPsychological interventionSelf-efficacyRandomized controlled trialAnxietyThe InternetGeneralized anxiety disorderPsychologyClinical psychologyResearch designApplied psychologyMedical educationPsychotherapistMedicinePsychiatryComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Generalized anxiety disorder (GAD) is one of the most common mental disorders among university students; however, many students go untreated due to treatment costs, stigma concerns, and limited access to trained mental health professionals. These barriers are heightened in universities in India, where there are scant mental health care services and severe stigma surrounding help seeking. OBJECTIVE: To evaluate the feasibility, acceptability, and efficacy of Internet-based, or "online," cognitive behavioral therapy (CBT)-based unguided and guided self-help interventions (using the programs GAD Online and Lantern, respectively) to reduce GAD symptoms in students with clinical and subthreshold GAD and, ultimately, reduce the prevalence and incidence of GAD among the student population. METHODS: Students will be recruited via 3 colleges in Hyderabad, India, and referred for a campus-wide online screening. Self-report data will be collected entirely online. A total of 300 qualifying students will be randomized in a 1:1:1 ratio to receive GAD Online, Lantern, or to be in a wait-list control condition, stratified by clinical and subthreshold GAD symptomatology. Students will complete a postintervention assessment after 3 months and a follow-up assessment 6 months later, at which point students in the wait-list control condition will receive one of the programs. The primary outcome is GAD symptom severity at 3 months postintervention. Secondary outcomes include GAD caseness at 9 months, other anxiety and depression symptoms, self-efficacy, and functional measures (eg, sleep, social functioning) at 3 and 9 months, respectively. Primary analyses will be differences between each of the intervention groups and the wait-list control group, analyzed on an intention-to-treat (ITT) basis using mixed-design ANOVA. RESULTS: The study commenced in February 2015. The sample was recruited over a 3-week period at each college. The trial is expected to end in December 2015. CONCLUSIONS: This trial will be the first to evaluate the use of Internet-based CBT programs compared with a wait-list control group for the treatment of GAD among students in Indian universities. If effective, these programs have the potential to reduce the mental health care treatment gap by providing readily accessible, private, and cost-effective evidence-based care to students with GAD who do not currently receive the treatment they need. TRIAL REGISTRATION: ClinicalTrials.gov NCT02410265 http://clinicaltrials.gov/ct2/show/NCT02410265 (Archived by WebCite at http://www.webcitation.org/6ddqH6Rbt).

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)0.020
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: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.021
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0020.001
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0100.002

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 head0.260
GPT teacher head0.559
Teacher spread0.299 · 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 designNot applicable
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

Citations9
Published2015
Admission routes1
Has abstractyes

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