Health Economists on Involving Patients in Modeling: Potential Benefits, Harms, and Variables of Interest
Bibliographic record
Abstract
BACKGROUND: Patient involvement in health economics modeling has been advocated on numerous grounds, including as a way to better manage social and ethical value judgments in the modeling process. However, some have pointed to potential risks and variables that could influence the overall benefit of involvement. To inform future research, there is a need to generate knowledge on potential benefits, harms, and variables relevant to patient involvement in health economics modeling. METHODS: This analysis used data from a qualitative study in which 22 health economists were asked their views on the possibility of involving patients in the modeling process. Using qualitative methods, the authors organized participants' responses into theory-driven categories ("potential benefits", "potential harms", "variables of interest") and identified data-driven themes and subthemes within those categories. RESULTS: Findings point to potential benefits and harms to the model, modeler, patient, and modeling process. Variables of interest relevant to future research included patients' specific roles, modeler and patient characteristics, the goals of modeling, dynamics among participators, and features of high-level procedures. The findings raise a number of specific questions that may be fruitful to explore in future research on patient involvement in health economics modeling.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".