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 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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".