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Record W3161302780 · doi:10.1136/bmjopen-2020-043124

Physical-mental multimorbidity in children and youth: a scoping review

2021· review· en· W3161302780 on OpenAlexaff
Isabella Romano, Claire Buchan, Leonardo Baiocco-Romano, Mark A. Ferro

Bibliographic record

VenueBMJ Open · 2021
Typereview
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMedicineMental healthMultimorbidityMental illnessPsychiatryComorbidity

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.648
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.260
GPT teacher head0.525
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations50
Published2021
Admission routes1
Has abstractyes

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