Perceived Healthiness of Breakfasts in Women with Overweight or Obesity Match Expert Recommendations
Bibliographic record
Abstract
Abstract The aim of the present study was to examine the perceived healthiness of breakfasts and the underlying beliefs influencing that perception against expert nutritional evaluation. Women with overweight or obesity (N = 120) were asked to recall the food items they consumed during a recent “healthy” or “unhealthy” breakfast. They also reported why the breakfast was healthy or unhealthy and rated its healthiness. Two nutritionists categorised the beliefs about why the breakfasts were “healthy” or “unhealthy” and evaluated the healthiness of each breakfast following nutrition guidelines. Generally, the meals considered as healthy versus unhealthy breakfasts and related beliefs about why the breakfasts were healthy or unhealthy matched food-based nutrition guidelines. Participants were found to perceive healthy breakfasts as more healthy and unhealthy breakfasts as less healthy than nutritionists did. Participants frequently mentioned the belief that their breakfast was healthy because “it contained fruit” or that their breakfast was unhealthy because “it contained fat.” Such salient healthy or unhealthy food items may guide the perception of breakfast healthiness and could be a target for nutrition counselling.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".