Food well-being: a review of its conceptualization and measurement
Bibliographic record
Abstract
Purpose This study aimed to improve understanding of the phenomenon of food well-being (FWB) (conceptualization, measurement, antecedents and outcomes) so as to lead future empirical work on measurement, development and theory testing. The hope is to improve the societal benefits of FWB and sustainable food system transformation. Design/methodology/approach A domain-based systematic review of FWB was conducted using databases (Web of Science, ABI/INFORM, EBSCO and Scopus). The well-established theory, context, characteristics and methodology framework were used to structure the review. Findings This study synthesized conceptual definitions and measurements of consumer FWB from hedonic, eudemonistic and mixed research streams and a nomological network that distinguishes this construct from its antecedents and outcomes. Practical implications This study provides recommendations for consumers, food designers, retailers and policymakers to improve FWB. Originality/value This study assessed the conceptualizations of FWB from hedonic, eudemonistic and mixed perspectives for conceptual clarity. It summarized ten measurement tools for FWB-allied concepts (Well-being Related to Food Questionnaires, Satisfaction with Food-Related Life Scale and World Health Organization-Five Well-Being Index), which revealed the need for novel measurement. This study developed a holistic nomological network of FWB by identifying the categories of antecedents (food-related, consumer-related and contextual factors) and outcomes (general well-being, life satisfaction and food consumption). This study provides a research agenda for FWB measurement and theoretical development.
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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".