Examination of the Item Structure of the Alberta Infant Motor Scale
Bibliographic record
Abstract
In Brief Purpose The Alberta Infant Motor Scale (AIMS) is a screening tool for identifying delayed motor development from birth to 18 months of age. The purpose of this study was to examine the psychometric structure of the AIMS, including the hierarchical scale of items and the precision for measuring infant ability at different ages. Methods Ninety-seven infants with varying degrees of risk of developmental disability were recruited from three hospitals or from the community in the Chicago metropolitan area. Infants were tested on the AIMS at three, six, nine, and 12 months of age. The hierarchical structure and the range and distribution of item difficulty on the AIMS were analyzed using Rasch psychometric analysis. Results The Rasch analysis confirmed that items for each of the four testing positions (supine, prone, sitting, and standing) were arranged in increasing order of difficulty, but a ceiling effect was present. Gaps exist at six ability levels, indicating low precision of measurement for differentiating among infants after about nine months of age. Conclusions The AIMS shows a ceiling effect, measures infant ability best from three to nine months of age, and has few items available for discriminating among infants after they pass the controlled lowering through standing item. Clinical impressions should be drawn with caution at ages when the precision of measurement is low. The AIMS shows a ceiling effect, measures infant ability best from three to nine months of age, and has few items available for discriminating among infants after they pass the controlled lowering through standing item. Clinical impressions should be drawn with caution at ages when the precision of measurement is low.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".