Attention deficit hyperactivity disorder in pre‐school children: current findings, recommended interventions and future directions
Bibliographic record
Abstract
This paper outlines the presentation, aetiology and treatment of attention deficit hyperactivity disorder (ADHD) in pre-school children. A review of current parenting training interventions demonstrates that there is good evidence for their efficacy in reducing symptoms of ADHD in pre-school children, and three interventions are evaluated: The new forest parent training programme (NFPP); the triple P - positive parenting programme and the incredible years parent training programme (IY). The evaluation of the NFPP provides strong evidence demonstrating its effectiveness for pre-school children with ADHD, while the efficacy of the Triple - P and the IY programme have, to date, only been demonstrated on children with conduct problems and co-morbid ADHD. It is suggested that parent training should be the first choice treatment for pre-school children presenting signs of ADHD, and medication introduced only for those children where parent training is not effective. Few moderators of outcome have been identified for these interventions, with the exception of parental ADHD. Barriers to intervention and implementation fidelity will need to be addressed to achieve high levels of attendance, completion and efficacy. The IY programme is a good model for addressing fidelity issues and for overcoming barriers to intervention. The future directions for parent training are also discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".