Physical-mental multimorbidity in children and youth: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Efforts to describe the current state of research are needed to advance the field of physical-mental multimorbidity (ie, the co-occurrence of at least one physical illness and at least one mental disorder) among children and youth. Our objective was to systematically explore the breadth of physical-mental multimorbidity research in children and youth and to provide an overview of existing literature topics. DESIGN: Scoping review. METHODS: We conducted a systematic search of four key databases: PubMed, EMBASE, PsycINFO and Scopus as well as a thorough scan of relevant grey literature. We included studies of any research design, published in English, referring to physical-mental multimorbidity among children and youth aged ≤18 years. Studies were screened for eligibility and data were extracted, charted and summarised narratively by research focus. Critical appraisal was employed using the modified Quality Index (QI). RESULTS: We included 431 studies and 2 sources of grey literature. Existing research emphasises the co-occurrence of anxiety, mood and attention disorders among children with epilepsy, asthma and allergy. Evidence consists of mostly small, observational studies that use cross-sectional data. The average QI score across applicable studies was 9.1 (SD=1.8). CONCLUSIONS: There is a pressing need for more robust research within the field of child physical-mental multimorbidity.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".