Developing Optimal Search Strategies for Retrieving Clinically Relevant Qualitative Studies in EMBASE
Bibliographic record
Abstract
Qualitative researchers address many issues relevant to patient health care. Their studies appear in an array of journals, making literature searching difficult. Large databases such as EMBASE provide a means of retrieving qualitative research, but these studies represent only a minuscule fraction of published articles, making electronic retrieval problematic. Little work has been done on developing search strategies for the detection of qualitative studies. The objective of this study was to develop optimal search strategies to retrieve qualitative studies in EMBASE for the 2000 publishing year. The authors conducted an analytic survey, comparing hand searches of journals with retrievals from EMBASE for candidate search terms and combinations. Search strategies reached peak sensitivities at 94.2% and peak specificities of 99.7%. Combining search terms to optimize the combination of sensitivity and specificity resulted in values over 89% for both. The authors identified search strategies with high performance for retrieving qualitative studies in EMBASE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.102 | 0.386 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.050 | 0.033 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".