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Record W3180759401 · doi:10.2196/20969

Designing Asynchronous Remote Support for Behavioral Activation in Teenagers With Depression: Formative Study

2021· article· en· W3180759401 on OpenAlexvenueno aff
Arpita Bhattacharya, Ria Nagar, Jessica L. Jenness, Sean A. Munson, Julie A. Kientz

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of Mental Health
KeywordsMental healthPsychological interventionThe InternetFormative assessmentPsychologyIntervention (counseling)MoodApplied psychologyAsynchronous communicationInternet privacyMedical educationMedicineClinical psychologyPsychiatryComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Many teenagers in the United States experience challenges with symptoms of depression, and they lack adequate resources for accessing in-person mental health care. Involving teens and clinicians in designing technologies that use evidence-based practices that reduce barriers to accessing mental health care is crucial. Interventions based on behavioral activation (BA) help teens understand the relationship between mood and activity, help them practice goal-directed behaviors to improve mood, and may be particularly well-suited to delivery via internet-based platforms. OBJECTIVE: This study aims to understand the needs and challenges that teens and mental health clinicians face in depression management and involve them in the design process of a remote intervention that uses asynchronous remote communities. Our goal is to understand the benefits and challenges of adapting BA to an internet-based platform that supports the asynchronous remote community approach as a delivery tool for teen depression management. METHODS: We enrolled mental health clinicians (n=10) and teens (n=8) in separate, private, internet-based groups on Slack (Slack Technologies Inc). They participated in 20-minute design activities for 10 weeks and were then invited to interviews about their experiences in the study. RESULTS: Both teen and clinician participants wanted internet-based support for BA as a supplement to in-person therapy. Although participants perceived the asynchronous format as conducive to supporting accessible care, teens and clinicians raised concerns about safety, privacy, and the moderating of the internet-based group. Design decisions that address these concerns need to be balanced with the potential benefits of learning coping skills, increasing access to mental health care, and promoting asynchronous human connection to support teens. CONCLUSIONS: We discuss considerations for balancing tensions in privacy and safety while designing and selecting internet-based platforms to support remote care and integrating evidence-based support when designing digital technologies for the treatment of teens with depression.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.144
GPT teacher head0.526
Teacher spread0.382 · 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 designQualitative
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

Citations16
Published2021
Admission routes1
Has abstractyes

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