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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.200 | 0.371 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".