The mental health needs of children and adolescents with learning disabilities
Bibliographic record
Abstract
PURPOSE OF REVIEW: To provide an update on the mental health needs of children and adolescents with learning disabilities, by examining salient studies published predominantly in the last 12-18 months. RECENT FINDINGS: There have been further articles published supporting the findings of earlier landmark studies demonstrating an increased prevalence of mental health disorders in young people with learning disabilities. These articles suggest higher rates of comorbidity than were previously recognized. There are few published studies pertaining to the effectiveness of psychological and pharmacological treatments, although there is a recognition that the latter are more routinely and perhaps inappropriately administered. Antipsychotics are the most commonly prescribed group of medications and, despite a lack of evidence, continue to be prescribed more to address challenging behaviours rather than in the treatment of an identified psychiatric disorder. Reviews examining services and policies in other countries further highlight that the health and social care needs of individuals with learning disabilities are receiving more attention, with a shared vision that services should be inclusive and preferably community based. SUMMARY: Although there is improved knowledge of the rates of mental health disorders in young people with learning disabilities, in clinical practice these mental health needs continue to be underrecognized and untreated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".