Development of the Assistance to Participate Scale (APS) for children's play and leisure activities
Bibliographic record
Abstract
AIM: This paper describes the development and psychometric evaluation of the Assistance to Participate Scale (APS). The APS measures the assistance that a school-aged child with a disability requires to participate in play and leisure activities from the primary carer's perspective. METHOD: Mixed methodology using an instrument design model was used to complete two studies. First, a qualitative research design was used to generate items and scoring criteria for the APS. Second, a quantitative study evaluated the instrument using data collected from 152 mothers with children aged 5-18 years. Statistical analysis assessed the underlying structure, internal consistency and construct validity of the APS. RESULTS: Exploratory factor analysis revealed two correlated components, reflecting home-based and community-based play activities. Both subscales and the total APS scale showed good internal consistency. The APS correlated as predicted with individual domains and overall scores for other validated measures (Pediatric Evaluation of Disability Inventory caregiver scales and Pediatric Quality of Life Inventory) with correlations ranging from rho = 0.42 to rho = 0.77. The APS was able to discriminate between groups of children based on type of schooling (regular or segregated), need for equipment/assistive devices, frequency of lifting and disability. CONCLUSIONS: The APS provides professionals with a brief psychometrically sound tool that measures the amount of caregiver assistance provided to a child with a disability to participate in play and recreation. The APS may be used as an outcome measure and to evaluate and predict the amount and type of additional assistance families need to facilitate their child's participation in an important aspect of the child's daily life and development: play and recreation.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".