A Systematic Review of Longitudinal Trajectories of Mental Health Problems in Children with Neurodevelopmental Disabilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it