Using Vignettes to Tap Into Moral Reasoning in Public Health Policy: Practical Advice and Design Principles From a Study on Food Advertising to Children
Bibliographic record
Abstract
In this article, we describe a process for designing and applying vignettes in public health policy research and practice. We developed this methodology for a study on moral reasoning underpinning policy debate on food advertising to children. Using vignettes prompted policy actors who were relatively entrenched in particular ways of speaking professionally about a controversial and ethically challenging issue to converse in a more authentic and reflective way. Vignettes hold benefits and complexities. They can focus attention on moral conflicts, draw out different types of evidence to support moral reasoning, and enable simultaneous consideration of real and ideal worlds. We suggest a process and recommendations on design features for crafting vignettes for public health policy.
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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 | 0.122 | 0.225 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".