Methods to assess ambivalence toward food and diet: a scoping review protocol.
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to identify and understand the different tools and methods used in studies in the field of human eating behavior to assess, measure, or classify participants' ambivalence toward food and diet, as well as to identify which tools and methods are most frequently employed. INTRODUCTION: People's attitudes toward food and eating behaviors are often ambivalent (simultaneously positive and negative), making it harder to change eating behaviors in favor of a healthier diet. This highlights the importance of resolving diet-related ambivalence. Identifying and understanding the different methods used in the literature to assess attitudinal ambivalence toward food and diet will provide researchers with a range of options to choose from for future studies. INCLUSION CRITERIA: We will include peer-reviewed studies as well as preprints that assess the ambivalence of human participants toward food and diet, regardless of sex, age, or other sociodemographic factors. We will exclude studies in which the methods used to assess ambivalence are not detailed or cannot be reproduced, as well as studies that assess the ambivalence of participants toward farming and agricultural methods or toward methods of food production and preparation. METHODS: This review will follow the JBI methodology for scoping reviews. Peer-reviewed studies will be retrieved from MEDLINE, PsycINFO, Web of Science, Food Science Source, FSTA, and CINAHL, while preprints will be retrieved from PsyArXiv and MedArXiv. Two independent reviewers will screen the articles. All relevant extracted information will be presented as tables and a descriptive summary of the findings.
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 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 | 0.159 | 0.124 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.017 |
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".