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Record W4285011468 · doi:10.3389/fpubh.2022.929473

Food Promotion and Children's Health: Considering Best Practices for Teaching and Evaluating Media Literacy on Food Marketing

2022· article· en· W4285011468 on OpenAlexafffund
Charlene Elliott, Emily Truman, Michelle R. Nelson, Cyndy Scheibe, Liselot Hudders, Steffi De Jans, Kara Brisson‐Boivin, Samantha McAleese, Matthew Johnson, Lauren M. Walker, Kirsten Ellison

Bibliographic record

VenueFrontiers in Public Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsUniversity of Calgary
KeywordsMedia literacyFood marketingLiteracyPromotion (chess)Public relationsStakeholderHealth literacyMarketingHealth promotionMedical educationPsychologyMedicinePedagogyPublic healthBusinessHealth carePolitical scienceNursing

Abstract

fetched live from OpenAlex

Food marketing to children is ubiquitous and persuasive. It primarily promotes foods of poor nutritional quality, influences children's food preferences and habits, and is a factor in childhood obesity. Given that food marketing relentlessly targets children in traditional/digital media and the built environment, children need critical media literacy skills that build their understanding of food marketing's persuasive effects. However, little research connects media literacy with food marketing and health, including effective strategies for teaching and evaluating such programming for children. This perspective presents the outcomes of a stakeholder meeting on best practices in teaching and evaluation on media literacy and food marketing to children. Strategies for promoting critical thinking (teaching content, teaching practices, teaching supports, and parent/caregiver involvement), and strategies for measuring critical thinking (program effectiveness and broader long-term impacts) were identified. These include, among other things, the need to capture the range of marketing formats and current food promotion trends , to include inquiry-based and co-creation activities , and to support ongoing media literacy development . Overall, these strategies suggest useful criteria for media literacy programming related to food marketing, and highlight the importance of media literacy for giving children the skills to navigate a complex food environment.

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)0.014
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.106
GPT teacher head0.385
Teacher spread0.279 · 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 designOther design
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

Citations7
Published2022
Admission routes2
Has abstractyes

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