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Record W2170823869 · doi:10.3109/01942638.2011.616266

Usual and Virtual Reality Video Game-based Physiotherapy for Children and Youth with Acquired Brain Injuries

2011· article· en· W2170823869 on OpenAlexaff
Danielle Levac, Patricia A. Miller, Cheryl Missiuna

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2011
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychological interventionThematic analysisRehabilitationIntervention (counseling)Virtual realityPopulationMotor skillMotor learningAcquired brain injuryPsychologyMedicineVideo gamePhysical therapyPhysical medicine and rehabilitationApplied psychologyMedical educationQualitative researchNursingMultimediaDevelopmental psychologyComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

Little is known about how therapists promote learning of functional motor skills for children with acquired brain injuries. This study explores physiotherapists' description of these interventions in comparison to virtual reality (VR) video game-based therapy. Six physiotherapists employed at a children's rehabilitation center participated in semi-structured interviews, which were transcribed and analyzed using thematic analysis. Physiotherapists describe using interventions that motivate children to challenge performance quality and optimize real-life functioning. Intervention strategies are influenced by characteristics of the child, parent availability to practice skills outside therapy, and therapist experience. VR use motivates children to participate, but can influence therapist use of verbal strategies and complicate interventions. Physiotherapists consider unique characteristics of this population when providing interventions that promote learning of motor skills. The VR technology has advantageous features but its use with this population can be challenging; further research is recommended.

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 score0.040
Threshold uncertainty score0.663

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 head0.037
GPT teacher head0.325
Teacher spread0.288 · 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

Citations57
Published2011
Admission routes1
Has abstractyes

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