About my Child: measuring ‘Complexity’ in neurodisability. Evidence of reliability and validity
Bibliographic record
Abstract
BACKGROUND: About my Child, 26-item version (AMC-26) was developed as a measure of child health 'complexity' and has been proposed as a tool for understanding the functional needs of children and the priorities of families. METHODS: The current study investigated the reliability and validity of AMC-26 with a sample of caregivers of children with neurodevelopmental disorders (NDD; n = 258) who completed AMC-26 as part of a larger study on parenting children with NDD. A subsample of children from the larger study (n = 49) were assessed using standardized measures of cognitive and adaptive functioning. RESULTS: Factor analysis revealed that a four-component model explained 51.12% of the variance. Cronbach's alpha was calculated for each of the four factors and for the scale as a whole, and ranged from 0.75 to 0.85, suggesting a high level of internal consistency. Construct validity was tested through comparisons with the results of standardized measures of child functioning. Predicted relationships for factors one, two and three were statistically significant and in the expected directions. Predictions for factor four were partially supported. AMC-26 was also expected to serve as an indicator of caregiver distress. Drawing on a sample of caregivers from the larger study (n = 251) the model was found to be significant and explained 23% of the variance in caregiver depressive symptoms (R(2) = .053, F (1, 249) = 14.06, P < .001). CONCLUSIONS: Based on these observations, the authors contend that AMC-26 may be used by clinicians and researchers as a tool to capture child function and child health complexity. Such a measure may help elucidate the relationships between child complexity and family well-being. This is an important avenue for further investigation.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".