Qualitative evidence, knowledge translation, and policy-making, with reference to health technology assessment
Bibliographic record
Abstract
Although efforts to draw qualitative evidence into health-related policy-making and health technology assessment (HTA) processes have increased in recent years, the range of sources consulted are still limited and the theoretical foundations for consulting them are underdeveloped. This essay builds on such recent scholarship, first, by opening conventional models of knowledge translation up to the possibilities of qualitative evidence, and second, by demonstrating the utility of this wider range of qualitative evidence, signally that of humanities scholarship, in health-related policy-making. The second of these will consist of two themes – pain and narrativity – that will illustrate both the particular complexity of policy-making in HTA, whereby social, ethical, and moral variables are at play, and the mitigating affect humanities scholarship, at its best, might have on this fraught process.
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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 | 0.203 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".