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Record W2174593867 · doi:10.3109/01942638.2015.1076559

Playfulness in Children with Limited Motor Abilities When Using a Robot

2015· article· en· W2174593867 on OpenAlexaff
Adriana Ríos Rincón, Kim Adams, Joyce Magill‐Evans, Al Cook

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2015
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCerebral palsyPsychologyGross motor skillMotor skillIntervention (counseling)Developmental psychologyTest (biology)Physical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

AIMS: Children with limited gross motor and manual abilities have fewer opportunities to engage in free play. We investigated the effect of a robotic intervention on the playfulness of children with cerebral palsy (CP). METHODS: We used a partially nonconcurrent multiple baseline design with four children and their mothers. Children were classified in level IV or V on the Gross Motor Function and Manual Ability Classification Systems. The intervention was the availability of an adapted Lego robot during a 15-min free play session between the child and mother. There were two sessions per week for about 14 weeks. Playfulness was measured using the Test of Playfulness. RESULTS: Statistical comparisons using the 2 SD band and X-moving range chart methods revealed that all the children's levels of playfulness increased significantly while they played with the robot. Comparison of baseline and follow-up phase indicated that three children had retention of improved level of playfulness. CONCLUSION: Play with adapted Lego robots increased the level of playfulness in all four children during free play with their mothers. The findings have implications for providing children with limitations in motor abilities opportunities for free play with family and friends.

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 score0.008
Threshold uncertainty score0.561

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 head0.066
GPT teacher head0.326
Teacher spread0.260 · 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

Citations56
Published2015
Admission routes1
Has abstractyes

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