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Record W2788617962 · doi:10.2196/mental.9441

Supporting Our Valued Adolescents (SOVA), a Social Media Website for Adolescents with Depression and/or Anxiety: Technological Feasibility, Usability, and Acceptability Study

2018· article· en· W2788617962 on OpenAlexvenueno aff
Ana Radović, Theresa Gmelin, Jing Hua, Cassandra Long, Bradley D. Stein, Elizabeth Miller

Bibliographic record

VenueJMIR Mental Health · 2018
Typearticle
Languageen
FieldPsychology
TopicMental Health via Writing
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of Mental HealthAgency for Healthcare Research and Quality
KeywordsUsabilitySocial mediaAnxietyDepression (economics)Social anxietyPsychologyClinical psychologyApplied psychologyComputer scienceWorld Wide WebData sciencePsychiatryHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: Supporting Our Valued Adolescents (SOVA), a social media website for adolescents, was designed to increase mental health literacy and address negative health beliefs toward depression and/or anxiety diagnosis and treatment. This stakeholder-informed site underwent iterative user testing to evolve into its current version with daily blog posts, round-the-clock site moderation, and Web-based peer interaction to create an online support community. OBJECTIVE: The aim of this study was to evaluate the technological feasibility (at least 100 users on the site, logging in 12 to 18 times in the first 6 weeks) and acceptability of the SOVA site determined by the System Usability Scale (SUS). METHODS: Adolescents and young adults (aged 14-26 years) with a self-reported history of depressive and/or anxiety symptoms were recruited to access the research website (sova.pitt.edu). Participants were screened out if they reported active suicidality or a prior suicide attempt. Baseline survey measures included demographics, symptomatology using the Patient Health Questionnaire-9 modified for adolescents (PHQ-9A) and Screen for Child Anxiety Related Disorders (SCARED-C), and mental health treatment history. The 6-week follow-up measures taken in addition to the symptomatology, included feasibility (total number of log-ins), usability, and acceptability of SOVA using SUS. RESULTS: Most of the 96 participants identified as female (75% [72/96]) and white (67% [64/96]). Most participants (73% [70/96]) reported having taken prior professional psychological help. The average PHQ-9A score was 11.8 (SD 5.5), and for SCARED-C, 85% (80/94) of the participants reported a score consistent with being susceptible to a diagnosed anxiety disorder. There were 46% (41/90) of eligible users who ever logged in. Out of the total users who ever logged in, the mean of total log-ins over the entire study was 4.1 (SD 6.9). Median number of users rated the user-friendliness of the site as "good." The average SUS score was 71.2% (SD 18.7), or a "C-grade," which correlated to an acceptable range. The participants reported to have liked the "easy-to-understand format" and "positive, helpful atmosphere," but they also reported a desire for greater social interaction. Iterative recruitment resulted in incremental improvements to the site. CONCLUSIONS: The SOVA site met feasibility goals of recruiting almost 100 users and establishing acceptable usability. Subsequent interventions are planned to increase site engagement and to evaluate efficacy in increasing uptake of primary care-recommended depression and/or anxiety treatment.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.456
Teacher spread0.388 · 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 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

Citations39
Published2018
Admission routes1
Has abstractyes

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