What is a snack, why do we snack, and how can we choose better snacks? The Definitions of Snacking, Motivations to Snack, Contributions to Dietary Intake, and Recommendations for Improvement
Bibliographic record
Abstract
Around the world, adults are consuming energy outside of breakfast, lunch, and dinner. However, because there is no consistent definition of a “snack,” it is unclear how these extra eating occasions should be labeled. The labels attributed to eating occasions can influence the other food choices an individual makes on the same day and their satiety after consumption as well as data collection and interpretation. Therefore, clear distinctions between “meals” and “snacks” are important for health outcomes and conducting research about them. Current literature suggests that the definition of and motivation to eat between meals depends on external factors like the time of day, type of food, food availability, and location but that definitions and motivations also vary widely even within a single population. The health impact of “snacking” also seems to be subject to considerable inter individual variation. Despite the paucity of research on the topic, eating between meals already contributes significantly to the daily energy intake of adults and children in several countries, including Brazil, Mexico, Canada, the United States, Greece, and France. With the exception of fruit, the most popular foods consumed at these occasions, chips, desserts, and sugar‐sweetened beverages, are high in nutrients to limit. Therefore, the foods selected for “snack” are of public health concern. Yet few countries include snack recommendations in their dietary guidelines because there is little evidence about the healthfulness of eating frequently. Nutrient insufficiencies and excesses in different countries could be used to make recommendations for specific snack foods without recommending snacking as an eating occasion. The development of more health‐promoting snacks could be an important area for collaboration between food companies and nutrition scientists. Promoting “healthy” options for snack time could benefit overall dietary intake especially in areas where snacking, regardless of its definition, is already popular. Support or Funding Information This study was supported by research funding from Kerry, Beloit, WI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".