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Record W2941119780 · doi:10.1177/0008417419831403

Sensory modulation, physical activity and participation in daily occupations in young children

2019· article· en· W2941119780 on OpenAlexvenueno aff
Dan Hertzog, Sharon A. Cermak, Tami Bar‐Shalita

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2019
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsSensory processingSensory systemPsychologyPhysical activityOccupational therapyDescriptive statisticsActivities of daily livingAssociation (psychology)Developmental psychologyMedicinePhysical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND.: Physical activity (PA) promotes children's health. While sensory processing is integral to motor output, research regarding PA in children with sensory processing challenges is sparse. PURPOSE.: This study aimed to examine the PA pattern and its association with daily life participation of children with sensory processing challenges. METHOD.: Forty-four children ages 5 to 7 years were divided into the study group (children with sensory processing challenges; n = 22; 16 males) and an age-matched typically developing control group ( n = 22; nine males). Parents completed the Short Sensory Profile, a demographic questionnaire, Participation in Childhood Occupations Questionnaire, and Participation in Physical Activity and Sedentary Behavior Questionnaire-Modified. Data were analyzed using descriptive statistics and correlational analysis. FINDINGS.: Structured PA was reported in 45% and 77% of the study and control groups, respectively ( p = .030). In the study group, the level of participation in PA was found to be significantly correlated with play and leisure activities. IMPLICATIONS.: Promoting structured and group PA opportunities may be important for children with sensory processing challenges.

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 score0.015
Threshold uncertainty score0.770

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 head0.057
GPT teacher head0.360
Teacher spread0.303 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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