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Record W4387077395 · doi:10.1111/obr.13623

Co‐creating obesity prevention policies with youth: Policy ideas generated through the CO‐CREATE project

2023· article· en· W4387077395 on OpenAlexaff
Kaitlin Conway‐Moore, Cécile Knai, Diane T. Finegood, Lee M. Johnston, Hannah Brinsden, Anaely Aguiar, Birgit Kopainsky, Furkan Önal, Arnfinn Helleve, Knut‐Inge Klepp, Nanna Lien, Aleksandra Łuszczyńska, Ana Isabel Rito, Alfred Mestad Rønnestad, Madeleine Ulstein, Laurence Blanchard, Natalie Savona, Harry Rutter

Bibliographic record

VenueObesity Reviews · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsSimon Fraser University
FundersHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsAction (physics)OverweightParticipatory action researchPolicy learningCitizen journalismObesityIntervention (counseling)PsychologyComputer scienceProcess managementPolitical sciencePublic relationsBusinessSociologyMedicine

Abstract

fetched live from OpenAlex

Despite growing recognition of the importance of applying a systems lens to action on obesity, there has only been limited analysis of the extent to which this lens has actually been applied. The CO-CREATE project used a youth-led participatory action research approach to generate policy ideas towards the reduction of adolescent overweight and obesity across Europe. In order to assess the extent to which these youth-generated policy ideas take a systems approach, we analyzed them using the Intervention Level Framework (ILF). The ILF ascribes actions to one of five system levels, from Structural Elements, the least engaged with system change, up to Paradigm, which is the system's deepest held beliefs and thus the most difficult level at which to intervene. Of the 106 policy ideas generated by young people during the CO-CREATE project, 91 (86%) were categorized at the level of Structural Elements. This emphasis on operational rather than systems level responses echoes findings from a previous study on obesity strategies. Analyzing the distribution of systems level responses using the ILF has the potential to support more effective action on obesity by allowing identification of opportunities to strengthen systems level responses overall.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.117
GPT teacher head0.382
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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations8
Published2023
Admission routes1
Has abstractyes

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