Factor- and item-level analyses of the 38-item Activities Scale for Kids-performance
Bibliographic record
Abstract
AIM: Children and adolescents highly value their ability to participate in relevant daily life and recreational activities. The Activities Scale for Kids-performance (ASKp) instrument measures the frequency of performance of 30 common childhood activities, and has been shown to be valid and reliable. A revised and expanded 38-item ASKp (ASKp38) version has been reported in recent literature and is currently used in clinical research. The aim of this paper is to assess the factor structure and item-level statistics of the ASKp38. METHOD: Our study used factor analyses and Rasch analyses to determine the item-set dimensionality and to calculate item-level statistics respectively, for existing ASKp38 data from 200 children (104 males; 96 females; mean age 12y 7mo; SD 2y 8mo; range 6-20y) with physical disabilities. The children had a variety of physical impairments including cerebral palsy (n = 105; range 8-13 y), limb salvage (n = 18; range 11-20y), arthrogryposis (n = 13; 6-17y), and other, including individuals with spina bifida and spinal cord injury (n = 64; 8-19 y). RESULTS: A two-factor model, with components of activities of daily living and play, most optimally fit the data. Item-fit statistics based on this two-factor model demonstrated adequate fit and content coverage. INTERPRETATION: The ASKp38 appears to consist of two factors, defined as (1) activities of daily living and (2) play, and may be used to measure the frequency of activity performance on two corresponding subscales.
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 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".