Understanding how healthcare providers build consumer trust in the Australian food system: A qualitative study
Bibliographic record
Abstract
AIM: This study aimed to identify how dietitians and other healthcare providers work to build trust in food systems in the course of providing dietary education. METHODS: Qualitative semi-structured interviews were conducted with 15 purposefully sampled dietitians (n = 5), general practitioners (n = 5), and complementary and alternative medicine practitioners (n = 5) within metropolitan South Australia. Interview data were then interpreted using an inductive thematic analysis approach, involving the construction of themes representing trust-enhancing roles around which beliefs about professional roles, the 'patient', and food and health were clustered. RESULTS: Healthcare providers communicate beliefs regarding (dis)trust in food systems through: (i) responding to patient queries and concerns following a food incident or scare; (ii) helping patients to identify (un)trustworthy elements of food supply systems; and (iii) encouraging consumption of locally produced and minimally processed food. Importantly, the expression of these roles differed according to participant beliefs about food and health (medico-scientific versus alternative medicine) and their adoption of professional projects that sought to promote medico-scientific ways of thinking about health and diet or manage the failures of Western medicine. CONCLUSION: The development and consolidation of trust-enhancing roles amongst healthcare providers likely requires disciplinary reflection on professional values and the processes by which practitioners apply these values to understanding food systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".