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Record W4386067518 · doi:10.2196/49092

Animated Videos Based on Food Processing for Guidance of Brazilian Adults: Validation Study

2023· article· en· W4386067518 on OpenAlexvenueno aff
Maria Fernanda Gomes da Silva, Luciana Neri Nobre, Edson da Silva

Bibliographic record

VenueInteractive Journal of Medical Research · 2023
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsScripting languageFood processingMultimediaComputer scienceProduction (economics)Consumption (sociology)Process (computing)PopulationIndex (typography)World Wide WebMedicineFood scienceEnvironmental healthSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Ultraprocessed foods (UPFs) contribute almost one-fifth of the calories consumed by the Brazilian population. This consumption has been favored by aspects such as the ease of acquisition and low cost of this food group. Initiatives focused on supporting and promoting healthy eating practices have been implemented. Among them, the availability of educational resources is an important strategy to maximize the effectiveness of these actions in the field of food and nutrition education (FNE). OBJECTIVE: This study aims to describe the development and validation process of animated videos based on the NOVA food classification for FNE actions aimed at Brazilian adults. METHODS: This methodological study was developed in the following 4 phases: planning, preproduction, production, and postproduction. In the planning phase, a literature review was con-ducted on the topic and to define the content to be covered. The design of the material was based on the cognitive theory of multimedia learning. In the preproduction phase, video scripts were developed and evaluated by 7 content specialists. In the production phase, videos were developed based on the assessed scripts and then assessed by 3 multimedia production specialists. In the postproduction phase, the videos were evaluated by 15 representatives of the target audience. All results obtained in the evaluation phases were analyzed using the content validity index (CVI). RESULTS: We developed 3 animated videos covering the following themes: food processing levels, food categories according to processing levels, and UPFs and their impact on health. In the evaluation by the content specialists, the scripts of videos 1, 2, and 3 obtained CVIs at the scale level and average method equal to 0.96, 0.98, and 0.98, respectively. When the animated videos were evaluated by multimedia production specialists and representatives of the target audience, these indexes were equal to 1.0. These results attest to the videos' adequacy and quality in communicating the addressed content. CONCLUSIONS: The animated videos developed and validated in this study proved to be adequate for their purpose. Thus, it is expected that they will be an important instrument for FNE actions aimed at an adult audience and for disseminating the Dietary Guidelines for the Brazilian Population.

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)0.006
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.109
GPT teacher head0.497
Teacher spread0.388 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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