Influence of reporting effects on the association between maternal depression and child autism spectrum disorder behaviors
Bibliographic record
Abstract
BACKGROUND: Maximizing measurement accuracy is an important aim in child development assessment and research. Parents are essential informants in the diagnostic process, and past research suggests that certain parental characteristics may influence how they report information about their children. This has not been studied in autism spectrum disorders (ASD) to date. We aimed, therefore, to investigate the possible effect that maternal depression might have on a mother's reports of her child's ASD behaviors. Using structural equation modeling, we disaggregated shared from unique variation in the association between latent variable measures of maternal depression and ASD behaviors. METHODS: Data were obtained from a study of preschoolers aged 2-4 newly diagnosed with ASD (n = 214). Information from a parent questionnaire, a semi-structured parent interview, and a semi-structured observational assessment was used to develop a latent variable measure of child ASD behaviors. Mothers reported on their own depression symptoms. We first modeled the covariance between maternal depression and child ASD behavior. Then, to quantify unique variation, we added covariance terms between maternal depression and the residual variation associated with the individual measures of child ASD behaviors. RESULTS: The model demonstrated excellent fit to the underlying data. Maternal self-report of depression symptoms exhibited a significant association with the unique variance of the questionnaire report but not with the latent variable measure of child ASD behavior. A gradient pattern of association was demonstrated between maternal depression and the unique variance of the ASD measures: most strongly for the maternal questionnaire report, more weakly for the maternal semi-structured interview, and to a trivial extent for the observational interview. CONCLUSIONS: Parental depression may influence reporting of ASD behaviors in preschoolers. Shared method effects may also contribute to bias. This finding highlights the importance of obtaining multimethod reports of child ASD symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".