MétaCan
Menu
Back to cohort
Record W2788745504 · doi:10.2196/resprot.8813

Implementation of the Enhanced Moderated Online Social Therapy (MOST+) Model Within a National Youth E-Mental Health Service (eheadspace): Protocol for a Single Group Pilot Study for Help-Seeking Young People

2018· article· en· W2788745504 on OpenAlexvenueno aff
Simon Rice, John Gleeson, Steven Leicester, Sarah Bendall, Simon D’Alfonso, Tamsyn Gilbertson, Eóin Killackey, Alexandra Parker, Reeva Lederman, Greg Wadley, Olga Santesteban‐Echarri, Ingrid Pryor, Daveena Mawren, Aswin Ratheesh, Mario Álvarez‐Jiménez

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research CouncilYoung and Well Cooperative Research CentreSociety for Mental Health Research
KeywordsMental healthProtocol (science)PsychologyMental health serviceMedical educationFocus groupService (business)Applied psychologyMedicinePsychiatrySociologyAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is a substantial need for youth electronic mental health (e-mental health) services. In addressing this need, our team has developed a novel moderated online social therapy intervention called enhanced moderated online social therapy (MOST+). MOST+ integrates real-time, clinician-delivered Web chat counseling, interactive user-directed online therapy, expert and peer moderation, and private and secure peer-to-peer social networking. MOST+ has been designed to give young people immediate, 24-hour access to anonymous, evidence-based, and short-term mental health care. OBJECTIVE: The primary aims of this pilot study were to determine the feasibility, acceptability, and safety of the intervention. Secondary aims were to assess prepost changes in key psychosocial outcomes and collect qualitative data for future intervention refinement. METHODS: MOST+ will be embedded within eheadspace, an Australian youth e-mental health service, and will be evaluated via an uncontrolled single-group study. Approximately 250 help-seeking young people (16-25 years) will be progressively recruited to the intervention from the eheadspace home page over the first 4 weeks of an 8-week intervention period. All participants will have access to evidence-based therapeutic content and integrated Web chat counseling. Additional access to moderated peer-to-peer social networking will be granted to individuals for whom it is deemed safe and appropriate, through a three-tiered screening process. Participants will be enrolled in the MOST+ intervention for 1 week, with the option to renew their enrollment across the duration of the pilot. Participants will complete a survey at enrollment to assess psychological well-being and other mental health outcomes. Additional assessment will occur following account deactivation (ie, after participant has opted not to renew their enrollment, or at trial conclusion) and will include an online survey and telephone interview assessing psychological well-being and experience of using MOST+. RESULTS: Recruitment for the study commenced in October 2017. We expect to have initial results in March 2018, with more detailed qualitative and quantitative analyses to follow. CONCLUSIONS: This is the first Australia-wide research trial to pilot an online social media platform merging real-time clinical support, expert and peer moderation, interactive online therapy, and peer-to-peer social networking. The importance of the project stems from the need to develop innovative new models for the efficient delivery of responsive evidence-based online support to help-seeking young people. If successful, this research stands to complement and enhance e-mental health services in Australia.

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)0.031
metaresearch head score (Gemma)0.015
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: Protocol
Teacher disagreement score0.037
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.015
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.001
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0030.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0370.007

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 head0.505
GPT teacher head0.641
Teacher spread0.136 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Research ProtocolsSame topicDigital Mental Health InterventionsFrench-language works237,207