PARENTAL PERCEPTIONS OF MEDICATION IMPACT ON BEHAVIOUR AND LEARNING IN CHILDREN WITH ATTENTION/DEFICIT-HYPERACTIVITY DISORDER
Bibliographic record
Abstract
Abstract BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that often negatively impacts behaviour, cognition, and learning. Stimulant medications are the most commonly used treatment for ADHD, with informant reports (e.g., parent questionnaires, surveys) frequently used to evaluate medication impact on behavioural and academic functioning in affected children. OBJECTIVES To determine parental perceptions of medication impact on behaviour and learning in a long-acting methylphenidate (LA-MPH) trial of their children with ADHD. DESIGN/METHODS A randomized controlled LA-MPH medication trial was conducted with children ages 8–12 with a diagnosis of ADHD. Trial began with one-week of baseline assessment, followed by a randomized three-week standard of care medication trial, and a one-week best dose assessment. Following the conclusion of the study (6 months to 2 years’ range post-treatment), families were asked to participate in a voluntary follow-up phone survey to evaluate parental perceptions on child’s behaviour and learning. RESULTS A total of 34/42 (81%) families participated (male to female ratio: 2.1/1). At the time of the follow-up survey, 53% (18/34) and 68% (23/34) of patients were having “difficulty” or “significant difficulty” with their behaviour and learning, respectively. Twenty-three patients (68%) were still on psycho-stimulant medications. Although parents of those 23 children said LA-MPH had in general “significant” or “very significant” impact on their child’s behaviour (87%) and learning (79%), these parents were still reporting challenges with behaviour (52%) and learning (61%) at follow-up. In addition, parents of children not on medication said that their current child’s behaviour and learning was still having the same or more challenging issues (82% and 73% respectively). CONCLUSION Overall, parents reported that medication significantly impacted their child’s behaviour and learning; however, long-term medication impact appears less effective. Thus, an ongoing relationship with families and paediatricians is recommended to better understand the impact of medication on behaviour and learning. Evaluating the effects of medication on behaviour and learning may ultimately lead to targeted intervention that help foster long-term treatment efficacy 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".