Perceived Healthiness of Sweeteners among Young Adults in Canada
Bibliographic record
Abstract
Purpose: To compare the perceived healthiness of different sweeteners relative to table sugar and examine efforts to consume less sugars and sweeteners. Methods: As part of the 2017 Canada Food Study online survey, 1000 youth and young adults were randomized to rate the healthiness of 1 of 6 sweeteners (aspartame, sucralose, stevia, agave, high-fructose corn syrup, “raw” sugar) or 1 sweetener brand name (Splenda) compared with “table sugar”. Results: Perceptions of sweeteners varied widely. For example, the majority of respondents perceived high-fructose corn syrup (63.9%) and aspartame (52.4%) as less healthy than table sugar, whereas almost half (47.8%) perceived raw sugar as being healthier than table sugar. No assessed socio-demographic variables were significantly associated with perceived healthiness of sweeteners compared with table sugar (P ≥ 0.05). More consumers had attempted to consume less sugar (65.4%) compared with less “artificial” (31.2%) or “natural” (24.0%) low-calorie sweeteners. Conclusions: Perceptions of sweetener healthiness may be related to sweeteners’ perceived level of “naturalness” rather than energy content. This has important implications for understanding consumer preferences, particularly given greater use of low-calorie sweeteners in the food supply and policy developments such as sugar taxes and enhanced sugar labelling.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".