Multiple needs and multiple treatments. What's a clinician to do? Update on the psychosocial treatment of disruptive behaviours in childhood
Bibliographic record
Abstract
PURPOSE OF REVIEW: There are a wide range of psychosocial treatment options, delivered in different modalities, for children with disruptive behaviour. However, clinicians face many challenges in ensuring the empirically supported treatments (ESTs) they select will be effective for their patient. This has prompted studies to generate knowledge on how to improve treatment outcomes for children with disruptive behaviour. This review identifies the major challenges in treatment selection as well as emerging research seeking to improve outcomes. RECENT FINDINGS: This review emphasizes the salience of the research-practice gap associated with establishing ESTs using narrow definitions of clinical problems. Recent research is reviewed considering the complex determinants of disruptive behaviours, including parent and family factors that influence outcomes. The review subsequently outlines recent advances in research and clinical practice guidelines aiming to surmount these challenges. Key advances discussed include examining the most impactful components of ESTs, personalizing interventions by targeting core dysfunction underlying behaviour, and addressing parent factors including mental health and cultural relevance to improve outcomes. SUMMARY: Thorough assessment of patients' needs, combined with knowledge of treatment response predictors, are recommended to determine the most suitable treatment plan. Recent advances have focused on developing and designing interventions that meet needs in a way that is flexible and tailored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".