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Record W4411549938 · doi:10.1136/bmjgh-2024-016167

Identifying key components of a global conceptual framework for adolescent nutrition: Review, nominal group technique and youth co-design

2025· review· en· W4411549938 on OpenAlexaff
Sara Estecha Querol, Catherine Fleming, Amir Ali Samnani, Milca J Cameseria, Sarah H Kehoe, Webster Isheanopa Makombe, Amanda Murungi Eunice, M. Raza, Marion Roche, Miriam Shindler, Deepika Sharma, Kesso Gabrielle van Zutphen, Stephanie V. Wrottesley

Bibliographic record

VenueBMJ Global Health · 2025
Typereview
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsNutrition International
FundersUNICEF
KeywordsKey (lock)Nominal group techniqueGroup (periodic table)Adolescent healthComponent (thermodynamics)Adolescent developmentNominal groupProcess managementPsychologyPolitical scienceComputer scienceMedicineBusinessKnowledge managementDevelopmental psychologyChemistryNursingComputer security

Abstract

fetched live from OpenAlex

BACKGROUND: Optimal nutrition during adolescence is critical to growth and development. While several adolescent nutrition frameworks exist, a comprehensive reference framework is needed, which reflects the priorities and views of young people and guides action to improve adolescent nutrition. We believe that partnering with young people is key to enabling agency and empowering change. This collaborative project between the Global Adolescent Nutrition Network (GANN) and youth partners aimed to develop a conceptual and actionable framework for adolescent nutrition. This paper presents the methods used to identify the need for and to prioritise the key components of a new framework. METHODS: A literature review was conducted to identify and summarise available frameworks for adolescent nutrition (10-19 years). GANN members (N=7) and youth partners (N=4, 18-26 years) used nominal group technique (NGT) methodology to reach consensus on: (1) key characteristics, strengths and limitations of available frameworks; (2) key characteristics of an ideal framework; (3) the best available framework and (4) the need for a new framework. RESULTS: NGT participants listed 37 strengths and limitations of the 15 frameworks identified in the review. These were classified into three themes: theory, usability and visibility. According to NGT participants, the most important ('top') theoretical features of an ideal framework were considering all forms of malnutrition' and including all levels of influence in adolescent nutrition'. Most top usability and visibility features were applicable at global, regional and country levels, and a clear structure to depict levels of influence, respectively. The Innocenti Framework was deemed the best available framework. Most of the participants (90%) agreed that a new framework was needed. Youth partners advocated for greater representation of youth voices in frameworks and programmes related to adolescents. The salient features of the new framework, reached by consensus, emphasise an action-oriented approach that provides practical guidance to improve adolescent nutrition rather than solely explaining its determinants. It will be designed to be adaptable to different contexts, ensuring its visual design, language and content are accessible and engaging for a broad range of stakeholders. CONCLUSION: Through a novel youth codesign process, we identified key components to inform the development of a new conceptual framework to improve adolescent nutrition. While no new framework is presented in this article and is part of ongoing work, these findings provide guidance for future efforts to create a framework that can effectively inform actions and investments in global adolescent nutrition.

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)0.152
metaresearch head score (Gemma)0.236
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.152
Threshold uncertainty score0.803

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1520.236
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0220.022
Science and technology studies0.0020.006
Scholarly communication0.0070.007
Open science0.0050.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.340
GPT teacher head0.583
Teacher spread0.243 · 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 designQualitative
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

Citations2
Published2025
Admission routes1
Has abstractyes

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