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Record W2788187081 · doi:10.2196/mental.8894

A Web-Based Psychoeducational Intervention for Adolescent Depression: Design and Development of MoodHwb

2018· article· en· W2788187081 on OpenAlexvenueno aff
Rhys Bevan Jones, Anita Thapar, Frances Rice, Harriet Beeching, Rachel Cichosz, Becky Mars, Daniel J. Smıth, Sally Merry, Paul Stallard, Ian Jones, Ajay K Thapar, Sharon Simpson

Bibliographic record

VenueJMIR Mental Health · 2018
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersNational Center for Mental HealthEconomic and Social Research CouncilMedical Research CouncilNational Institute for Health and Care ResearchLlywodraeth CymruHealth and Care Research WalesAmerican Foundation for Suicide Prevention
KeywordsPsychoeducationPsychological interventionPsychosocialIntervention (counseling)PsychologyDistressMedicinePsychiatryClinical psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Depression is common in adolescence and leads to distress and impairment in individuals, families and carers. Treatment and prevention guidelines highlight the key role of information and evidence-based psychosocial interventions not only for individuals but also for their families and carers. Engaging young people in prevention and early intervention programs is a challenge, and early treatment and prevention of adolescent depression is a major public health concern. There has been growing interest in psychoeducational interventions to provide accurate information about health issues and to enhance and develop self-management skills. However, for adolescents with, or at high risk of depression, there is a lack of engaging Web-based psychoeducation programs that have been developed with user input and in line with research guidelines and targeted at both the individual and their family or carer. There are also few studies published on the process of development of Web-based psychoeducational interventions. OBJECTIVE: The aim of this study was to describe the process underlying the design and development of MoodHwb (HwbHwyliau in Welsh): a Web-based psychoeducation multimedia program for young people with, or at high risk of, depression and their families, carers, friends, and professionals. METHODS: The initial prototype was informed by (1) a systematic review of psychoeducational interventions for adolescent depression; (2) findings from semistructured interviews and focus groups conducted with adolescents (with depressive symptoms or at high risk), parents or carers, and professionals working with young people; and (3) workshops and discussions with a multimedia company and experts (in clinical, research, and multimedia work). Twelve interviews were completed (four each with young people, parents or carers, and professionals) and six focus groups (three with young people, one with parents and carers, one with professionals, and one with academics). RESULTS: Key themes from the interviews and focus groups were: aims of the program, design and content issues, and integration and context of the program. The prototype was designed to be person-centered, multiplatform, engaging, interactive, and bilingual. It included mood-monitoring and goal-setting components and was available as a Web-based program and an app for mobile technologies. CONCLUSIONS: MoodHwb is a Web-based psychoeducational intervention developed for young people with, or at high risk of, depression and their families and carers. It was developed with user input using qualitative methods as well as user-centered design and educational and psychological theory. Further research is needed to evaluate the effectiveness of the program in a randomized controlled trial. If found to be effective, it could be implemented in health, education, youth and social services, and charities, to not only help young people but also families, carers, friends, and professionals involved in their care.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.629

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 head0.074
GPT teacher head0.455
Teacher spread0.381 · 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 designOther design
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

Citations67
Published2018
Admission routes1
Has abstractyes

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