A Multiple Indicators Multiple Causes (MIMIC) model of friendship quality and comorbidities in children with attention-deficit/hyperactivity disorder.
Bibliographic record
Abstract
The unique objectives of the current investigation were: (a) to assess the fit of a multiinformant 2-factor measurement model of friendship quality in a clinical sample of children with attention-deficit/hyperactivity disorder (ADHD); and (b) to use a multiple indicators multiple causes approach to evaluate whether comorbid externalizing and internalizing disorders incrementally predict levels of positive and negative friendship quality. Our sample included 165 target children diagnosed with ADHD (33% girls; aged 6-11 years). Target children, their parents, their friends, and the parents of their friends independently completed a self-report measure of friendship quality about the reciprocated friendship between the target child and the friend. Results indicated that a multiinformant 2-factor measurement model with correlated positive friendship quality and negative friendship quality had good fit. The friendships of children with ADHD and a comorbid externalizing disorder were characterized by less positive friendship quality and more negative friendship quality than the friendships of children with ADHD and no externalizing disorder after controlling for the presence of a comorbid internalizing disorder. However, the presence of a comorbid internalizing disorder did not predict positive or negative friendship quality. These findings suggest that soliciting reports from parents in addition to children and friends, and measuring comorbid externalizing disorders, may be valuable evidence-based strategies when assessing friendship quality in ADHD populations. (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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".