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Record W3112896843 · doi:10.3148/cjdpr-2020-030

Perceived Healthiness of Sweeteners among Young Adults in Canada

2020· article· en· W3112896843 on OpenAlexafffundvenueabout
Samantha Goodman, Lana Vanderlee, Amanda Jones, Christine M. White, David Hammond

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2020
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsUniversité LavalUniversity of Waterloo
FundersCanadian Institutes of Health Research
KeywordsSucraloseSugarAspartameArtificial SweetenerHigh-fructose corn syrupAdded sugarFood scienceSaccharinFructoseMedicineChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.353
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2020
Admission routes4
Has abstractyes

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