Brazilian Validation of the Alberta Infant Motor Scale
Bibliographic record
Abstract
BACKGROUND: The Alberta Infant Motor Scale (AIMS) is a well-known motor assessment tool used to identify potential delays in infants' motor development. Although Brazilian researchers and practitioners have used the AIMS in laboratories and clinical settings, its translation to Portuguese and validation for the Brazilian population is yet to be investigated. OBJECTIVE: This study aimed to translate and validate all AIMS items with respect to internal consistency and content, criterion, and construct validity. DESIGN: A cross-sectional and longitudinal design was used. METHODS: A cross-cultural translation was used to generate a Brazilian-Portuguese version of the AIMS. In addition, a validation process was conducted involving 22 professionals and 766 Brazilian infants (aged 0-18 months). RESULTS: The results demonstrated language clarity and internal consistency for the motor criteria (motor development score, α=.90; prone, α=.85; supine, α=.92; sitting, α=.84; and standing, α=.86). The analysis also revealed high discriminative power to identify typical and atypical development (motor development score, P<.001; percentile, P=.04; classification criterion, χ(2)=6.03; P=.05). Temporal stability (P=.07) (rho=.85, P<.001) was observed, and predictive power (P<.001) was limited to the group of infants aged from 3 months to 9 months. LIMITATIONS: Limited predictive validity was observed, which may have been due to the restricted time that the groups were followed longitudinally. CONCLUSIONS: In sum, the translated version of AIMS presented adequate validity and reliability.
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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".