Methods to assess ambivalence towards food and diet: a scoping review
Bibliographic record
Abstract
BACKGROUND: Ambivalence towards food and diet, which favours behavioural inertia, might be a barrier to adopting healthier eating behaviours. Measuring it can help researchers to better understand its relationship with behaviour change and design interventions aimed at resolving it. In this scoping review, we map and describe methods and tools employed in studies to assess, measure or classify the ambivalence of participants towards food- and diet-related attitude objects. METHODS: In accordance with Joanna Briggs Institute guidance for conducting scoping reviews, we retrieved peer-reviewed studies from MEDLINE, CINAHL, PsycINFO, Web of Science, FSTA and Food Science Source and preprints from PsyArXiv and MedRxiv. Two independent reviewers screened the articles. We considered for inclusion peer-reviewed studies and preprints that assessed the ambivalence of participants of any age, sex or sociodemographic group towards food and diet. RESULTS: We included 45 studies published between 1992 and 2022, which included participants from 17 countries. Eighteen methods were employed across the included studies to assess different types of ambivalence (felt, potential or cognitive-affective), the most frequent of which were the Griffin Index, the Subjective Ambivalence Questionnaire, the MouseTracker Paradigm and the Orientation to Chocolate Questionnaire. CONCLUSION: This scoping review identified several methods and tools to assess different types of ambivalence towards food- and diet-related objects, providing an array of options for future studies.
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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 | 0.078 | 0.209 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.056 | 0.039 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".