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Record W4312120988 · doi:10.2196/43949

Participatory System Dynamics Approach Targeting Childhood Health in a Small Danish Community (Children’s Cooperation Denmark): Protocol for a Feasibility Study Design

2022· article· en· W4312120988 on OpenAlexvenueno aff
Knud Ryom, Helene Kirkegaard, Steven Allender, Anna Aaby, Christina Breddam, Helle Terkildsen Maindal, Jane Nautrup Østergaard

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsnot available
FundersSteno Diabetes Center AarhusNovo Nordisk Fonden
KeywordsDanishProtocol (science)Participatory designCitizen journalismParticipatory action researchCommunity-based participatory researchMedicineComputer scienceSociologyEngineeringAlternative medicineWorld Wide WebOperations management

Abstract

fetched live from OpenAlex

BACKGROUND: Improving childhood health is complex due to the multifactorial nature and interaction of determinants. Complex problems call for complex intervention thinking, and simple one-size-fits-all solutions do not work to improve childhood health. Early awareness is important, as behavior in childhood often is manifested across adolescence and into adulthood. To facilitate shared understanding of the complex structures and relationships that determine children's health behavior, participatory system approaches in, for example, local communities have shown promising potential. However, such approaches are not used systematically within public health in Denmark, and before being rolled out, they should be tested for their feasibility within this context. OBJECTIVE: This paper describes the study design for Children's Cooperation Denmark (Child-COOP) feasibility study that is aiming to examine the feasibility and acceptability of the participatory system approach and the study procedures for a future scale-up controlled trial. METHODS: The feasibility study is designed as a process evaluation of the intervention with the use of both qualitative and quantitative methods. A local childhood health profile will provide data for childhood health issues, for example, daily physical activity behavior, sleep patterns, anthropometry, mental health, screen use, parental support, and leisure-time activities. Data at system level are collected to assess development in the community, for example, readiness to change, analysis of social networks with stakeholders, rippled effects mapping, and changes in system map. The setting is a small rural town in Denmark, Havndal, with children as the primary target group. Group model building, a participatory system dynamics method, will be used to engage the community, create consensus on the drivers of childhood health, identify local opportunities, and develop context-specific actions. RESULTS: The Child-COOP feasibility study will test the participatory system dynamics approach for intervention and evaluation design and survey objective measures of childhood health behavior and well-being among the ~100 children (6-13 years) attending the local primary school. Community-level data will also be collected. We will assess the contextual factors, implementation of interventions, and mechanisms of impact as part of a process evaluation. Data will be collected at baseline, at 2 years, and 4 years of follow-up. Ethical approval for this study was sought and granted from the Danish Scientific Ethical Committee (1-10-72-283-21). CONCLUSIONS: s: The potential of this participatory system dynamics approach includes opportunities for community engagement and local capacity building to improve children's health and health behavior, and this feasibility study holds the potential to prepare an upscaling of the intervention for effectiveness testing. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43949.

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)0.078
metaresearch head score (Gemma)0.055
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 score0.078
Threshold uncertainty score0.414

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0780.055
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.003
Science and technology studies0.0060.004
Scholarly communication0.0030.003
Open science0.0030.004
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0560.009

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 head0.749
GPT teacher head0.620
Teacher spread0.129 · 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".

Quick stats

Citations12
Published2022
Admission routes1
Has abstractyes

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