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Record W4407270081 · doi:10.1136/bmjopen-2024-095721

Microsimulation models on child and adolescent health: a scoping review protocol

2025· review· en· W4407270081 on OpenAlexaff
Claire de Oliveira, Filipa Sampaio

Bibliographic record

VenueBMJ Open · 2025
Typereview
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsMental Health Research CanadaPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
FundersUppsala Universitet
KeywordsMedicineProtocol (science)MicrosimulationChild healthPublic healthHealth services researchFamily medicineAlternative medicineNursingPathology

Abstract

fetched live from OpenAlex

Introduction Microsimulation models are computer-based models, which can be employed to simulate the behaviour of microagents, such as children and adolescents, to understand the potential behavioural and economic effects of health interventions or policies. As a result, these models can be useful tools to help guide decision-making. A comprehensive review of the literature on child and adolescent microsimulation models has yet to be undertaken. Moreover, an evaluation of the quality of the existing models can be useful to understand their strengths and limitations and thus inform the development of future models. The aim of this scoping review will be to retrieve, synthesise and critically appraise the literature on existing microsimulation models focused on child and adolescent health. Methods and analysis We will conduct a scoping review using established methods. We will search PubMed (until 23 September 2024), Embase (until 18 September 2024), CINAHL (until 9 September 2024), PsyclNFO (11 September 2024), EconLit (until 9 September 2024) and Scopus (until 10 September 2024), with an update closer to the time of manuscript submission. We will also undertake snowballing, Google searches and searches on specific journal (eg, International Journal of Microsimulation ) and websites (eg, https://www.microsimulation.ac.uk/ ) to complement database searches. We will extract relevant data on all studies retrieved and use the Quality Assessment Reporting for Microsimulation Models checklist to assess the reporting quality of each model. We will use a narrative synthesis with summary tables to describe our findings. Findings will be synthesised by type of health condition, if/where possible. Ethics and dissemination Given that primary data will not be collected in this study, research ethics approval is not required. We will present our findings at relevant conferences and publish our results in an appropriate peer-reviewed academic journal. In addition, we will use this information to guide the development of a microsimulation model on child and adolescent health for use in the Swedish context. Registration details https://osf.io/a8txn/

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 imitation

Not 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.

metaresearch head score (Codex)0.119
metaresearch head score (Gemma)0.133
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.119
Threshold uncertainty score0.630

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1190.133
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0140.017
Bibliometrics0.0290.022
Science and technology studies0.0040.005
Scholarly communication0.0100.009
Open science0.0070.009
Research integrity0.0090.005
Insufficient payload (model declined to judge)0.0670.013

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 head0.577
GPT teacher head0.636
Teacher spread0.060 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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