What’s beyond the core? Database coverage in qualitative information reveal
Bibliographic record
Abstract
Objective: This study investigates the effectiveness of bibliographic databases to retrieve qualitative studies for use in systematic and rapid reviews in Health Technology Assessment (HTA) research. Qualitative research is becoming more prevalent in reviews and health technology assessment, but standardized search methodologies-particularly regarding database selection-are still in development. Methods: To determine how commonly used databases (MEDLINE, CINAHL, PsycINFO, Scopus, and Web of Science) perform, a comprehensive list of relevant journal titles was compiled using InCites Journal Citation Reports and validated by qualitative researchers at Canada's Drug Agency (formerly CADTH). This list was used to evaluate the qualitative holdings of each database, by calculating the percentage of total titles held in each database, as well as the number of unique titles per database. Results: While publications on qualitative search methodology generally recommend subject-specific health databases including MEDLINE, CINAHL, and PsycINFO, this study found that multidisciplinary citation indexes Scopus and Web of Science Core Collection not only had the highest percentages of total titles held, but also a higher number of unique titles. Conclusions: These indexes have potential utility in qualitative search strategies, if only for supplementing other database searches with unique records. This potential was investigated via tests on qualitative rapid review search strategies translated to Scopus to determine how the index may contribute relevant literature.
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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 | 0.532 | 0.853 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.030 | 0.040 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.023 | 0.053 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".