Introduction and validation of a naturalistic observational hand skill assessment for use in Australian early childhood contexts
Bibliographic record
Abstract
Purpose of the studyUse of hand skills plays a vital role in children’s development andparticipation in daily occupations and learning activities. This study will introducethe development and validation of a naturalistic observational instrument, calledthe Assessment of Children’s Hand Skills (ACHS). The intention of the ACHS is toobtain a truer, more realistic indication of children’s hand skill performance byobserving them in their home or school contexts. The inter-rater and test-retestreliability and construct and convergent validity of the ACHS were examined.MethodologyThe ACHS was established through literature and expert review. A group of138 Australian children (including 85 typically-developing children and 53 childrenwith disabilities) were recruited to examine its reliability and validity. The RaschMeasurement Model was utilised for analysis.Overall findingsThe ACHS’s test-retest reliability was satisfactory while its inter-rater reliabilitywas unacceptable. Construct validity or unidimensionality of the ACHS wasestablished after one hand skill item was eliminated. The children’s ACHS scoreswere significantly associated with their fine motor and personal living skill scores;thus confirming its convergent validity.ImplicationsEarly childhood-related professionals could use the revised ACHS as anoutcome measure to assess and monitor children’s hand skills. Further work isneeded to improve the ACHS’s inter-rater reliability.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".