Turkish Adaptation and Psychometric Properties of the Standardized Infant Neurodevelopmental Assessment Neurological Scale in Turkish At-Risk Infants
Bibliographic record
Abstract
Background: Early identification and intervention of neurodevelopmental delays can significantly improve outcomes for infants. Therefore, having a standardized assessment tool is essential for clinicians and healthcare professionals working in this field. Objectives: The aim of this study was to assess the concurrent validity and reliability of the Turkish adaptation of the Standardized Infant Neurodevelopmental Assessment (SINDA) neurological scale. Methods: In the study, 111 infants (46 females) participated. Construct validity for the SINDA neurological scale was determined through confirmatory factor analysis, while concurrent validity was established by examining the correlation between the SINDA neurological scale and the Alberta Infant Motor Scale and the Hammersmith Infant Neurological Examination using Spearman's correlation analysis. Additionally, the test-retest reliability of the SINDA scale was examined, and the intraclass correlation coefficient (ICC) was calculated. Results: Construct validity (RMSEA = 0.050; GFI = 0.93) and concurrent validity (r = 0.19 - 0.78; p < 0.05) of the SINDA neurological scale were acceptable. Confirmatory factor analysis results supported the six-factor structure of the original scale. High Cronbach’s alpha and ICC values were found (Cronbach’s α 0.74 - 0.81, ICC 0.991-0.997). Additionally, we found low to high positive correlations of SINDA with HINE and AIMS. Conclusions: The SINDA neurological scale exhibits strong psychometric qualities, making it a reliable and valid instrument for evaluating the neurodevelopmental aspects of at-risk Turkish infants. This has important implications for clinical practice, as early identification and intervention of neurodevelopmental delays can significantly improve outcomes for infants.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".