Perceived Benefits and Barriers Surrounding Lentil Consumption in Families with Young Children
Bibliographic record
Abstract
PURPOSE: Plant-based diets are advocated for prevention of chronic diseases. Lentils are an inexpensive plant-based meat alternative. This study determined perceived benefits and barriers to lentil consumption and how they relate to the demographics and nutritional knowledge of caregivers and consumption habits in families with children 3-11 years of age. METHODS: A self-administered questionnaire measuring nutritional knowledge and perceived benefits and barriers to the consumption of lentils was completed by 401 caregivers in a school setting in Saskatoon, Saskatchewan. RESULTS: The majority of respondents were 26-45 years of age (83%) and female (76%). Respondents associated lentils with health benefits (91%). The most frequently reported barrier associated with consumption pertained to family acceptance: "if my child liked lentils I would make them more" (76% agreement). More than half (58%) of respondents stated they "never or rarely" consumed lentils (low-consumers). Of low-consumers, top barriers included lack of knowledge on how to cook lentils and a belief that family members would not accept lentils. CONCLUSIONS: Future promotion strategies should address the top barriers to lentil consumption. An understanding of the perceived benefits and barriers surrounding lentil consumption will help formulate approaches to increase consumption of lentils as well as pulses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".