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Record W2931338337 · doi:10.25035/ijare.10.04.06

Examining the Perceived Impacts of Recreational Swimming Lessons for Children with Autism Spectrum Disorder

2019· article· en· W2931338337 on OpenAlexaff
Erin Kraft

Bibliographic record

VenueInternational Journal of Aquatic Research and Education · 2019
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRecreationPsychologyAutism spectrum disorderAutismApplied psychologyCertificationDevelopmental psychologyMedical educationMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the perceived impacts of recreational swimming lesson participation for children with Autism Spectrum Disorder (ASD). Although swimming lessons are a suitable form of physical activity for children with ASD, minimal research has examined the impacts of these lessons. The author conducted semi-structured interviews with an Applied Behaviour Analysis (ABA) certified therapist and a swim instructor, each with experience working with children with ASD in swimming lessons. The participants suggested that swimming lessons encouraged children with ASD to socialise. Both participants agreed that distractions in swimming lessons and barriers in communication created challenges for developing swimming skills. Finally, the participants described techniques they found appropriate for teaching children with ASD. These results aim to provide insights into the perceived impacts of recreational swimming lessons and appropriate techniques for lessons. Hopefully these insights may inspire parents/guardians of children with ASD to include swimming lessons into the routines of their children while also considering the safety risks of aquatic environments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.074
GPT teacher head0.399
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2019
Admission routes1
Has abstractyes

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