Finding Qualitative Research Evidence for Health Technology Assessment
Bibliographic record
Abstract
Health technology assessment (HTA) agencies increasingly use reviews of qualitative research as evidence for evaluating social, experiential, and ethical aspects of health technologies. We systematically searched three bibliographic databases (MEDLINE, CINAHL, and Social Science Citation Index [SSCI]) using published search filters or "hedges" and our hybrid filter to identify qualitative research studies pertaining to chronic obstructive pulmonary disease and early breast cancer. The search filters were compared in terms of sensitivity, specificity, and precision. Our screening by title and abstract revealed that qualitative research constituted only slightly more than 1% of all published research on each health topic. The performance of the published search filters varied greatly across topics and databases. Compared with existing search filters, our hybrid filter demonstrated a consistently high sensitivity across databases and topics, and minimized the resource-intensive process of sifting through false positives. We identify opportunities for qualitative health researchers to improve the uptake of qualitative research into evidence-informed policy making.
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 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 | 0.634 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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".