Search Strategies for Identifying Qualitative Studies in CINAHL
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nurses, allied health professionals, clinicians, and researchers increasingly use online access to evidence in the course of patient care or when conducting reviews on a particular topic. Qualitative research has an important role in evidence-based health care. Online searching for qualitative studies can be difficult, however, resulting in the need to develop search filters. The objective of this study was to develop optimal search strategies to retrieve qualitative studies in CINAHL for the 2000 publishing year. The authors conducted an analytic survey comparing hand searches of journals with retrievals from CINAHL for candidate search terms and combinations. Combinations of search terms reached peak sensitivities of 98.9% and peak specificities of 99.5%. Combining search terms optimized both sensitivity and specificity at 94.2%. Empirically derived search strategies combining indexing terms and textwords can achieve high sensitivity and high specificity for retrieving qualitative studies from CINAHL.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.262 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it