Developing Optimal Search Strategies for Retrieving Qualitative Studies in PsycINFO
Bibliographic record
Abstract
Researchers and practitioners have problems retrieving qualitative studies. Search strategies that can easily and effectively retrieve these studies from large databases such as PsycINFO are therefore important. To determine if search strategies can identify qualitative studies, 64 journals published in 2000 were hand searched using explicit methodological criteria to identify qualitative studies. The authors tested multiple search strategies using 4,985 potential search terms in PsycINFO (Ovid Technologies) and compared the results with the hand search data to calculate retrieval effectiveness. A total of 125 qualitative studies were identified. Single-term and multiple-term strategies had sensitivities (maximizing retrieval of qualitative studies) up to 94.4% and specificities (minimizing retrieval of nonqualitative studies and reports) up to 98.6% with ranges of precision and accuracy. Search strategies included terms that were variations of interview, qualitative, themes, and experience. Formal indexing terms performed poorly. Empirically derived search strategies combining textwords can effectively, but not perfectly, retrieve qualitative studies from PsycINFO.
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 | 0.182 | 0.512 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.057 | 0.051 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".