Have infant gross motor abilities changed in 20 years? A re‐evaluation of the <scp>A</scp>lberta <scp>I</scp>nfant <scp>M</scp>otor <scp>S</scp>cale normative values
Bibliographic record
Abstract
AIM: To compare the original normative data of the Alberta Infant Motor Scale (AIMS) (n=2202) collected 20 years ago with a contemporary sample of Canadian infants. METHOD: This was a cross-sectional cohort study of 650 Canadian infants (338 males, 312 females; mean age 30.9 wks [SD 15.5], range 2 wks-18 mo) assessed once on the AIMS. Assessments were stratified by age, and infants proportionally represented the ethnic diversity of Canada. Logistic regression was used to place AIMS items on an age scale representing the age at which 50% of the infants passed an item on the contemporary data set and the original data set. Forty-three items met the criterion for stable regression results in both data sets. RESULTS: The correlation coefficient between the age locations of items on the original and contemporary data sets was 0.99. The mean age difference between item locations was 0.7 weeks. Age values from the original data set when converted to the contemporary scale differed by less than 1 week. INTERPRETATION: The sequence and age at emergence of AIMS items has remained similar over 20 years and current normative values remain valid. Concern that the 'back to sleep' campaign has influenced the age at emergence of gross motor abilities is not supported.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".