MétaCan
Menu
Back to cohort
Record W4399137799 · doi:10.1093/heapro/daae043

The centrality of food in Norwegian adolescents’ life; a photo elicitation study among Norwegian youth

2024· article· en· W4399137799 on OpenAlexaff
Helene Aronsen-Kongerud, Sheri Bastien, Knut‐Inge Klepp

Bibliographic record

VenueHealth Promotion International · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNorwegianCentralityAgency (philosophy)Food choicePhoto elicitationSustainabilityPsychologyAutonomyQualitative researchAffect (linguistics)Environmental healthMarketingBusinessMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

The purpose of the study was to explore how adolescents from a high school in Viken county define and interact with food systems in their immediate environments to understand if and how health and sustainability affect their food choices. A qualitative case study design and a participatory approach were employed. Data were collected through photo elicitation combined with group interviews. Pictures were analyzed in collaboration with participants, and the group interview through systematic text condensation. Results indicate that adolescents perceive food systems as being a substantial part of their everyday life, that they care about their health and that of the planet, and they wish to take sustainability and health into consideration when making food choices. Their food choices are affected by aspects such as family, friends, marketing, price, time, availability and accessibility. They perceive that their agency to influence their own diet and food systems is limited. Adolescents hold unique and important knowledge of their food-related behaviors and value their autonomy to make food choices. Future research and policies aiming to help adolescents make healthy and sustainable food choices should therefore actively include adolescents.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.313
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueHealth Promotion InternationalSame topicEnvironmental Education and SustainabilityFrench-language works237,207