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Record W3088876051 · doi:10.1017/s0007114520003773

A systematic review of the determinants of seafood consumption

2020· review· en· W3088876051 on OpenAlexaboutno aff
Sophie Govzman, Sarah Looby, X. Wang, Francis Butler, Eileen R. Gibney, Claire Timon

Bibliographic record

VenueBritish Journal Of Nutrition · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)Environmental healthPsychological interventionMedicine

Abstract

fetched live from OpenAlex

Although seafood is considered to be an important part of a balanced diet, many national food consumption surveys suggest that seafood is not consumed in sufficient amounts. As consumers are moving to diversify their diet from animal-based protein, it is important to understand the factors influencing consumption of marine foods. This review aims to assess the characteristics of seafood consumers as well as the influences on seafood consumption in Europe, USA, Canada, Australia and New Zealand. Systematic search strategies were used to identify relevant journal articles from three electronic databases (PubMed, Web of Science and Embase). Three searches were carried out and identified 4405 unique publications from which 121 met the criteria for the review process. The reviewed studies revealed that seafood consumers were more likely to be older, more affluent and more physically active and were less likely to smoke compared with non-seafood consumers. Sex and BMI did not appear to have a directional association with seafood consumption. The most commonly reported barriers to seafood consumption were cost, followed by sensory or physical barriers, health and nutritional beliefs, habits, availability and cooking skills. The most commonly reported influences were beliefs about the contribution of seafood to health, environmental influences and personal preferences. Based on the findings of this review, future intervention strategies to increase seafood consumption may need to consider affordability and education in terms of health, nutrition and cooking skills. More research is needed to explore the effectiveness of specific interventions at increasing the consumption of seafood.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.064
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations103
Published2020
Admission routes1
Has abstractyes

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