Assessment of heterogeneity in types of vegetables served by main household food preparers and food decision influencers
Bibliographic record
Abstract
OBJECTIVE: While vegetables are often studied as one food group, global measures may mask variation in the types and forms of vegetables preferred by different individuals. To explore preferences for and perceptions of vegetables, we assessed main food preparers based on their preparation of eight specific vegetables and mushrooms. DESIGN: An online self-report survey. SETTING: Ontario, Canada. Measures included perceived benefits and obstacles of vegetables, convenience orientation and variety seeking in meal preparation. SUBJECTS: Of the 4517 randomly selected consumers who received the invitation, 1013 responded to the survey (22·4 % response). Data from the main food preparers were analysed (n 756). RESULTS: Latent profile analysis indicated three segments of food preparers. More open to new recipes, the 'crucifer lover' segment (13 %) prepared and consumed substantially more Brussels sprouts, broccoli and asparagus than the other segments. Although similar to the 'average consumer' segment (54 %) in many ways, the 'frozen vegetable user' segment (33 %) used significantly more frozen vegetables than the other segments due to higher prioritization of time and convenience in meal preparation and stronger 'healthy=not tasty' perception. Perception of specific vegetables on taste, healthiness, ease of preparation and cost varied significantly across the three consumer segments. Crucifer lovers also differed with respect to shopping and cooking habits compared with the frozen vegetable users. CONCLUSIONS: The substantial heterogeneity in the types of vegetables consumed and perceptions across the three consumer segments has implications for the development of new approaches to promoting these foods.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".