Predicting Use of Medications for Children with ADHD: The Contribution of Parent Social Cognitions.
Bibliographic record
Abstract
OBJECTIVE: To explore how well parental social cognitions, guided by the Theory of Reasoned Action (TRA), contribute to the uptake and continuation of stimulant medication for children with ADHD. No previous study has explored this model in predicting medication use in a clinical sample. METHOD: Sixty-nine parents of children aged 6-13 years presenting to a tertiary ADHD clinic completed questionnaires, and a clinician documented their medication usage. RESULTS: When controlling for medication status at baseline, both of the components of the TRA (i.e., attitudes and norms) predicted medication status following initial visit. Logistic regressions indicated that parents were more likely to enroll in or continue stimulant medication if they had lower stigma related to ADHD, a higher opinion of ADHD medications, and a greater knowledge of ADHD; this model classified 72.5% of the patients who started or continued stimulant medications. CONCLUSIONS: Findings suggest that the parents' knowledge about ADHD, opinion about treatment, and ADHD-related stigma are key factors to target in order to increase the uptake and continued use of evidence-based pharmacological interventions for children with ADHD.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".