Rapid qualitative evidence syntheses (rQES) in health technology assessment: experiences, challenges, and lessons
Bibliographic record
Abstract
Healthcare decision makers are increasingly demanding that health technology assessment (HTA) is patient focused, and considers data about patients' perspectives on and experiences with health technologies in their everyday lives. Related data are typically generated through qualitative research, and in HTA the typical approach is to synthesize primary qualitative research through the conduct of qualitative evidence synthesis (QES). Abbreviated HTA timelines often do not allow for the full 6-12 months it may take to complete a QES, which has prompted the Canadian Agency for Drugs and Technologies in Health (CADTH) to explore the concept of "rapid qualitative evidence synthesis" (rQES). In this paper, we describe our experiences conducting three rQES at CADTH, and reflect on challenges faced, successes, and lessons learned. Given limited methodological guidance to guide this work, our aim is to provide insight for researchers who may contemplate rQES. We suggest several lessons, including strategies to iteratively develop research questions and search for eligible studies, use search of filters and limits, and use of a single reviewer experienced in qualitative research throughout the review process. We acknowledge that there is room for debate, though believe rQES is a laudable goal and that it is possible to produce a quality, relevant, and useful product, even under restricted timelines. That said, it is vital to recognize what is lost in the name of rapidity. We intend our paper to advance the necessary debate about when rQES may be appropriate, and not, and enable productive discussions around methodological development.
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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 | 0.791 | 0.840 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.036 | 0.050 |
| Open science | 0.012 | 0.043 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".