The relationship between children’s sensory processing patterns and their leisure preferences and participation patterns
Bibliographic record
Abstract
BACKGROUND: Sensory processing patterns may be associated with children's preferences for different activities; however, knowledge about how different sensory processing patterns may relate to children's participation in leisure activities is scarce. PURPOSE: This study investigated in what leisure activities children with extreme sensory processing patterns participate and if relationships exist between children's sensory processing patterns and their leisure preferences and participation patterns. METHOD: This correlational study analyzed data from children's Sensory Profiles and reported play and leisure preferences. All 91 children in the sample completed the Children's Assessment for Participation and Enjoyment (CAPE) and the Preferences for Activities of Children (PAC). Parents of children ages 6 to 10 years completed the Sensory Profile, and children ages 11 to 14 years completed the Adolescent/Adult Sensory Profile. FINDINGS: Children with different sensory processing patterns preferred both similar and distinct leisure activities. Low-registration quadrant summary z scores negatively correlated with CAPE overall diversity scores (rs=-.23, p=.03), sensitivity quadrant summary z scores negatively correlated with preferences for social activities (rs=-.23, p=.03) and preferences for skill-based activities (rs=-.22, p=.04), and avoiding quadrant summary z scores negatively correlated with preferences for social activities (rs=-.26, p=.01). IMPLICATIONS: Children's sensory preferences are related to leisure preferences and participation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".