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Record W2038565269 · doi:10.3109/17518423.2013.799244

The leisure activity settings and experiences of youth with severe disabilities

2013· article· en· W2038565269 on OpenAlexafffund
Gillian King, Beata Batorowicz, Patty Rigby, Madhu Pinto, Laura Thompson, Freda Goh

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2013
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalMcMaster UniversityUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsRecreationPsychologyLeisure activityApplied psychologyLeisure timePhysical activityDevelopmental psychologySocial psychologyMedicinePhysical therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective was to describe the leisure activity settings of youth with severe disabilities, the environmental qualities of these settings, and youths' experiences. METHODS: Fifteen youth using augmentative and alternative communication and 11 with complex continuing care needs took part in 54 leisure activity settings of their own choosing. Following their participation, they completed the Self-Reported Experiences of Activity Settings questionnaire and trained observers completed the Measure of Environmental Qualities of Activity Settings. RESULTS: Youths' selected activity settings provided relatively high opportunities for choice, interaction with adults, and social activities, and youth experienced relatively high levels of psychological engagement, social belonging, and control and choice. Youth primarily took part in activity settings that provided opportunities for competency/relatedness and involved others. CONCLUSION: Implications for future research and clinical practice include the importance of valuing passive recreational activities for the opportunities for challenge, choice, and social interaction they provide.

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 score0.139
Threshold uncertainty score0.234

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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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

Citations30
Published2013
Admission routes2
Has abstractyes

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