The health implications of distrust in the food system: findings from the dimensions of trust in food systems scale (DOTIFS scale)
Bibliographic record
Abstract
BACKGROUND: Consumer trust in food systems is essential for consumers, food industry, policy makers and regulators. Yet no comprehensive tool for measuring consumer trust in food systems exists. Similarly, the impact that trust in the food system has on health-related food behaviours is yet to be empirically examined. The aim of this research was to develop a comprehensive instrument to measure trust in the food system (the Dimensions of Trust in Food Systems Scale (DOTIFS scale) and use it to explore whether trust in the food system impacts consumers' health-related behaviours. METHODS: The DOTIFS scale was developed using sociological theories of trust and pre-existing instruments measuring aspects of trust. It was pilot tested and content validity was assessed with 85 participants. A mixed-methods exploration of the health-related behaviours of 18 conveniently sampled Australian consumers with differing trust scores determined by the DOTIFS scale was then conducted. During March-July 2019 shopping- and home-observations were used to assess participants' food safety practices and exposure to public health fortification programs, while the CSIRO Healthy Diet Score determined their adherence to national dietary guidelines. RESULTS: The DOTIFS scale was found to have high comprehension, ease of use and content validity. Statistical analysis showed scale scores significantly trended as predicted by participants' stated level of trust. Differences were found in the way individuals with more or less trust in the food system comply with national dietary guidelines, are exposed to public health fortification programs, and adhere to recommended food safety practices. CONCLUSIONS: The DOTIFS scale is a comprehensive, sociologically- and empirically- informed assessment of consumer trust in food systems that can be self-administered online to large populations and used to measure changes in consumer trust over time. The differences in health-related behaviours between individuals with varying levels of trust warrant further investigation.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".