Social, ethical, and other value judgments in health economics modelling
Bibliographic record
Abstract
Modelling is a major method of inquiry in health economics. In other modelling-intensive fields, such as climate science, recent scholarship has described how social and ethical values influence model development. However, no similar work has been done in health economics. This study explored the role of social, ethical, and other values in health economics modelling using philosophical theory and qualitative interviews in British Columbia, Canada. Twenty-two professionals working in health economics modelling were interviewed between February and May, 2019. The study findings provide support for four philosophical arguments positing an essential role for social and ethical values throughout scientific inquiry and demonstrate how these arguments apply to health economics modelling. It highlights the role of social values in informing early modelling decisions, shaping model assumptions, making trade-offs between desirable model features, and setting standards of evidence. These results point to several decisions in the modelling process that warrant focus in future health economics research, particularly that which aims to incorporate patient and public values.
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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 | 0.103 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.070 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| 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".