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Record W3154713148 · doi:10.2196/27475

Preliminary Outcomes of an Ecological Momentary Intervention for Social Functioning in Schizophrenia: Pre-Post Study of the Motivation and Skills Support App

2021· article· en· W3154713148 on OpenAlexvenueno aff
Daniel Fulford, David E. Gard, Kim T. Mueser, Jasmine Mote, Kathryn Gill, Lawrence Leung, Jessica Mow

Bibliographic record

VenueJMIR Mental Health · 2021
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionPsychologySocial competenceSocial skillsSocial supportSchizophrenia (object-oriented programming)Quality of life (healthcare)Intervention (counseling)Clinical psychologymHealthDevelopmental psychologyPsychiatryPsychotherapistSocial change

Abstract

fetched live from OpenAlex

BACKGROUND: People with schizophrenia and other serious mental illnesses often lack access to evidence-based interventions, particularly interventions that target meaningful recovery outcomes such as social functioning and quality of life. Mobile technologies, including smartphone apps, have the potential to provide scalable support that places elements of evidence-based interventions at the palm of patients' hands. OBJECTIVE: We aim to develop a smartphone app-called Motivation and Skills Support-to provide targeted social goal support (eg, making new friends and improving existing relationships) for people with schizophrenia enrolled in a stand-alone open trial. METHODS: In this paper, we presented preliminary outcomes of 31 participants who used the Motivation and Skills Support app for 8 weeks, including social functioning pre- to postintervention, and momentary reports of treatment targets (eg, social motivation and appraisals) during the intervention. RESULTS: The findings suggest that the intervention improved self-reported social functioning from baseline to treatment termination, particularly in female participants. Gains were not maintained at the 3-month follow-up. Furthermore, increased social functioning was predicted by momentary reports of social appraisals, including perceived social competence and the extent to which social interactions were worth the effort. CONCLUSIONS: The implications of these findings and future directions for addressing social functioning in schizophrenia using mobile technology have been discussed. TRIAL REGISTRATION: ClinicalTrials.gov NCT03404219; https://clinicaltrials.gov/ct2/show/NCT03404219.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.030
GPT teacher head0.400
Teacher spread0.370 · 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 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

Citations34
Published2021
Admission routes1
Has abstractyes

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