Qualitative methodologies in health‐care priority setting research
Bibliographic record
Abstract
Priority setting research in health economics has traditionally employed quantitative methodologies and been informed by post-positivist philosophical assumptions about the world and the nature of knowledge. These approaches have been rewarded with well-developed and validated tools. However, it is now commonly noted that there has been limited uptake of economic analysis into actual priority setting and resource allocation decisions made by health-care systems. There seem to be substantial organizational and political barriers. The authors argue in this paper that understanding and addressing these barriers will depend upon the application of qualitative research methodologies. Some efforts in this direction have been attempted; however these are theoretically under-developed and seldom rooted in any of the established qualitative research traditions. Two such approaches - narrative inquiry and discourse analysis - are highlighted here. These are illustrated with examples drawn from a real-world priority setting study. The examples demonstrate how such conceptually powerful qualitative traditions produce distinctive findings that offer unique insight into organizational contexts and decision-maker behavior. We argue that such investigations offer untapped benefits for the study of organizational priority setting and thus should be pursued more frequently by the health economics research community.
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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 | 0.107 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".