A Systematic Review of Longitudinal Trajectories of Mental Health Problems in Children with Neurodevelopmental Disabilities
Bibliographic record
Abstract
Abstract To review the longitudinal trajectories – and the factors influencing their development – of mental health problems in children with neurodevelopmental disabilities. Systematic review methods were employed. Searches of six databases used keywords and MeSH terms related to children with neurodevelopmental disabilities, mental health problems, and longitudinal research. After the removal of duplicates, reviewers independently screened records for inclusion, extracted data (outcomes and influencing factors), and evaluated the risk of bias. Findings were tabulated and synthesized using graphs and a narrative. Searches identified 94,662 unique records, from which 49 publications were included. The median publication year was 2015. Children with attention deficit hyperactivity disorder were the most commonly included population in retrieved studies. In almost 50% of studies, trajectories of mental health problems changed by < 10% between the first and last time point. Despite multiple studies reporting longitudinal trajectories of mental health problems, greater conceptual clarity and consideration of the measures included in research is needed, along with the inclusion of a more diverse range of populations of children with neurodevelopmental disabilities.
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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".