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Record W4287958524 · doi:10.1177/15394492221108389

Educational Technologies for Teaching Social Skills to Individuals With Schizophrenia: Scoping Review

2022· article· en· W4287958524 on OpenAlexaff
Nicole Surdyka, Amy S. Clark, Andrea Duncan

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2022
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCINAHLSchizophrenia (object-oriented programming)Psychological interventionContext (archaeology)PsychologySocial skillsMedical educationQuality of life (healthcare)Applied psychologyClinical psychologyPsychiatryMedicinePsychotherapist

Abstract

fetched live from OpenAlex

Schizophrenia interventions incorporate improving quality of life and social functioning. Educational technologies are a potential treatment method for social skills development among individuals with schizophrenia. The objective of the study is to provide an overview of the characteristics and range of approaches of educational technologies in the context of social skills for individuals with schizophrenia. A scoping review methodological framework was applied. Search strategy was conducted on Ovid MEDLINE® and CINAHL Plus. Data were synthesized using a charting form for a logical, descriptive summary of results. The search yielded 771 results and 23 included studies that met eligibility criteria. The data showed persons with schizophrenia respond well to educational technologies to address illness self-management. Using technology in conjunction with traditional evidence-based interventions demonstrates promising results to improve social skills functioning. Occupational therapists can use educational technologies to decrease the gap in health care services and improve social support for individuals with schizophrenia.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.101
GPT teacher head0.484
Teacher spread0.383 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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