Evaluating maternal psychopathology biases in reports of child temperament: An investigation of measurement invariance.
Bibliographic record
Abstract
Parent reports of child temperament, especially those of mothers', are frequently used in research and clinical practice, but there are concerns that maternal characteristics, including a history of psychopathology, might bias reports on these measures. However, whether maternal reports of youth temperament show structural differences based on mothers' psychiatric history is unclear. We therefore conducted tests of measurement invariance to examine whether maternal psychopathology was associated with structural aspects of child temperament as a means of evaluating potential biases related to mothers' mental disorder history. From 2 community-based studies of child temperament, 935 mothers completed the Child Behavior Questionnaire (CBQ) and semistructured diagnostic interviews that assessed their own lifetime history of depressive symptoms, anxiety, and substance use disorders. Mothers also completed a measure of depressive symptoms concurrent to their completion of the CBQ. We found little evidence that mothers' current depressive symptoms or history of depressive symptoms, anxiety, or substance use disorders were associated with the structure of their reports of child temperament. Thus, there is little empirical support for systematic biases in reports of youth temperament as indexed by psychometric modeling. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.044 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".