Multimorbidity in children and youth: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Multimorbidity (co-occurring physical and mental illness) is an important issue for clinicians and researchers with combined efforts aimed at promoting the health and well-being of individuals across the life course. In children and youth, experience of any chronic physical illness leads to a substantial increase in risk for mental illness. As a growing field of interest, research is needed to map the current state of the literature in child and youth multimorbidity in order to identify existing gaps and inform the direction of future investigations. METHODS AND ANALYSIS: . A systematic search of the following four key databases will be conducted: (1) PubMed; (2) EMBASE; (3) PsycINFO; and (4) Scopus, using combinations of Medical Subject Headings (MeSH) and Emtree terms. We will also consult grey literature sources and hand-search reference lists of included studies to identify additional studies of relevance. For eligible studies that meet all identified inclusion and exclusion criteria, a data extraction tool will be used to collect and store key study characteristics that will be relevant for collating, summarising and reporting the results of the scoping review. This scoping review also presents a novel use of quality index scoring, which we anticipate will contribute to strengthening the rigour of the scoping review methodology. ETHICS AND DISSEMINATION: The proposed scoping review does not require ethical approval. Final study results will be disseminated via conference presentations, publication in a peer-reviewed journal and knowledge translation activities with relevant stakeholders.
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 | 0.128 | 0.091 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.065 | 0.015 |
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".