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Record W2547833281 · doi:10.1556/066.2016.45.4.2

Consumers knowledge about dietary fibre — Results of a survey questionnaire in Hungary and Romania

2016· article· en· W2547833281 on OpenAlexaboutno aff
Viktória Szűcs, Zita Fazakas, M. Tarcea, Raquel P. F. Guiné

Bibliographic record

VenueActa Alimentaria · 2016
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)Quarter (Canadian coin)RomanianPerceptionBusinessQuestionnaireMarketingThe InternetPsychologyAgricultural scienceAdvertisingGeographySociologyBiologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

Dietary fibres (DFs) are essential components of the balanced diet. Even though the adequate level of their consumption can be ensured from several natural (e.g. fruit, vegetables, legumes) and ‘artificial’ sources (e.g. functional foods), the consumed levels are below the recommendations. To analyse the Hungarian and Romanian consumers’ knowledge level, their perceptions of the health benefits associated with fibre, as well as the recognition of the potential information sources, a survey questionnaire was conducted with the total of 713 consumers. Results showed that the level of knowledge about DFs was not adequate. Internet was found to be widely used and identified as one of the most appropriate information sources to encourage the consumption of DF. It was a favourable result that three-quarter of the respondents was interested in the topic of healthy food consumption; however, just less than half of them took into consideration the label information during their shopping decisions. To increase the consumption of DF and to support the responsibility and conscious consumer decisions steps must to be done (e.g. education of children, pointing out of the sources). For this purpose, modern information technology and communication channels fitting to the consumers’ cultural and personal particularities can be utilized.

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)0.000
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 score0.068
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.034
GPT teacher head0.299
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.

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

Citations3
Published2016
Admission routes1
Has abstractyes

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