THE EXTENT AND QUALITY OF QUALITATIVE EVIDENCE INCLUDED IN HEALTH TECHNOLOGY ASSESSMENTS: A REVIEW OF SUBMISSIONS TO NICE AND CADTH
Bibliographic record
Abstract
OBJECTIVES: Qualitative methods allow in-depth exploration of patient experiences and can provide context for healthcare decision making. Frameworks for patient-based evidence in health technology assessment (HTA) are expanding; yet, how extensively qualitative methods are currently used is unclear. This review characterized the extent and quality of qualitative data submitted to National Institute for Health and Care Excellence (NICE) and Canadian Agency for Drugs and Technologies in Health (CADTH) for HTA. METHODS: NICE and CADTH submissions from September 2019 to August 2021 were reviewed. Submission characteristics and features of patient-based evidence included within submissions were extracted. The quality of qualitative reporting was assessed using the CASP checklist. RESULTS: Patient-based evidence was included in 83/107 NICE and 119/124 CADTH submissions. A small proportion described qualitative data collection (NICE=14; CADTH=24) and analysis (NICE=6; CADTH=9) methods. One-to-one interviews were the most common data collection method, and thematic analysis was exclusively used. Thirty-three percent of NICE submissions scored >7 yes responses on CASP, versus 78 percent of CADTH submissions. CONCLUSIONS: Although patient-based evidence was common in the submissions reviewed, only 14/107 NICE and 24/124 CADTH submissions involved formal qualitative data collection. Use of formal analysis methods was even rarer and reporting tended to be brief. At present, there is little guidance about qualitative evidence most likely to be informative and therefore to potentially impact decision making. Ensuring, however, that qualitative data are collected and analyzed in a systematic, rigorous way will maximize their usefulness and ensure that patient voices are clearly heard.
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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 | 0.529 | 0.838 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.050 | 0.040 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".