Frameworks for Synthesizing Qualitative Evidence in Health Technology Assessment: A Scoping Review Protocol
Bibliographic record
Abstract
Health Technology Assessment (HTA) agencies and researchers recognize that evidence-based methodologies should be based on more than just quantitative evidence. Qualitative data enable evaluation based on feasibility, appropriateness, meaningfulness, patient values and preferences, acceptability, and equity. Despite some guidelines explaining how to conduct evidence synthesis of qualitative data, a specific framework or guideline for the HTA recommendation process is only recent and requires clarification . This scoping review aims to describe the frameworks, tools, and processes used to synthesize qualitative evidence and rate the quality of HTA. This scoping review follows the JBI methodology. The databases accessed were Medline (Pubmed), LILACS, CINAHL, Embase, Web of Science, Scopus, PsycINFO, Cochrane Library, JBI Database, and ScienceDirect. Grey literature was searched on PROQUEST, Open Grey, CADTH’s Grey Matters, Google Scholar, and HTA Agencies’ websites. The inclusion criterion was the synthesis of qualitative evidence frameworks as a concept, which refers to methods to synthesize evidence and rate the quality of evidence. HTA is applied worldwide, and there is no specific population . Data are presented in a tabular format and include fundamental concepts, frameworks, methods, subjects, and objectives.
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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 | 0.270 | 0.213 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.031 | 0.028 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.087 | 0.022 |
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".