Mapping frameworks for synthesizing qualitative evidence in health technology assessment
Bibliographic record
Abstract
OBJECTIVES: Health Technology Assessment (HTA) practitioners recognize the significance of qualitative methodologies that focus on how a technology is feasible, meaningfulness, acceptable, and equitable. This mapping aimed to delineate the frameworks employed to synthesize qualitative evidence and assess the quality of synthesis in HTA . METHODS: Mapping was conducted using Medline, LILACS, CINAHL, Embase, Web of Science, Scopus, PsycINFO, Cochrane Library, JBI, and ScienceDirect databases. Gray literature searches included PROQUEST, Open Grey, Canadian Agency for Drugs and Technologies in Health's Grey Matters, Google Scholar, and HTA agency websites. The inclusion criteria were centered on global qualitative evidence synthesis frameworks. The data are presented in the tables. RESULTS: Of the 2054 articles, 31 were included, mostly from Europe. Guide was the type of document more cited, and most authors are from HTA agencies and universities. Incorporating both patient and family perspectives is the most cited reason for include qualitative evidence. Regardless of the framework or tool, SPICE was the main acronym, and RETREAT was preferred for approach selection. Thematic synthesis dominated analytic methods, and CASP was the primary quality appraisal tool. GRADE-CERQual graded evidence synthesis, with ENTREQ as the top reporting guidance. The GRADE evidence-to-decision framework was mentioned for recommendations. CONCLUSION: This mapping highlights the movement incorporate qualitative evidence in HTA employing specific frameworks. Despite the similarities among documents, most of them describe part of the process to synthesize qualitative evidence. Standardizing procedures to incorporate qualitative evidence into HTA can enhance decision-making. These findings offer essential considerations for HTA practice.
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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 | 0.503 | 0.590 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.060 | 0.051 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".