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Record W2788218542 · doi:10.12965/jer.1835168.584

Effects of virtual reality exercise for Korean adults with schizophrenia in a closed ward

2018· article· en· W2788218542 on OpenAlexaff
Garam Jo, Brenda Rossow-Kimball, Gwitaek Park, Yongho Lee

Bibliographic record

VenueJournal of Exercise Rehabilitation · 2018
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPhysical fitnessSchizophrenia (object-oriented programming)Physical therapyTest (biology)PsychologyRegimenIntervention (counseling)MedicinePhysical medicine and rehabilitationGerontologyPsychiatry

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the effects of virtual reality exercise (VRE) using Nintendo Wii-Fit on physical fitness of Korean adults with schizophrenia living in a mental health facility located in South Korea. Two male participants diagnosed with schizophrenia, ages 53 and 61, were recruited and selected for inclusion in this study. The intervention using the Nintendo Wii-Fit consisted of 35-min sessions, 3 times per week for 8 weeks and was facilitated by the primary researcher and two graduate students. The senior fitness test and 10-m walking test were used to measure the physical functioning, specifically, physical fitness and mobility, of the participants. The study was divided into three phases using an A-B-A single-subject design and involved multiple repeated measures of functional physical fitness. Both participants were evaluated each week for the duration of 18 weeks. Both participants exhibited measureable improvement in some of the physical fitness measures, but not in the mobility. These results thus provide preliminary evidence to support the use of VRE to improve physical function for Korean adults with schizophrenia as an alternative exercise regimen to the conventional exercise.

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)0.002
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 score0.770
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.267
Teacher spread0.261 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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