Optimal CINAHL Search Strategies for Identifying Therapy Studies and Review Articles
Bibliographic record
Abstract
PURPOSE: To design optimal search strategies for locating sound therapy studies and review articles in CiNAHL in the year 2000. DESIGN AND METHODS: An analytic survey was conducted, comparing hand searches of 75 journals with retrievals from CINAHL for 5,020 candidate search terms and 17,900 combinations for therapy and 5,977 combinations for review articles. All articles were rated with purpose and quality indicators. Candidate search strategies were used in CINAHL, and the retrievals were compared with results of the hand searches. The proposed search strategies were treated as "diagnostic tests" for sound studies and the manual review of the literature was treated as the "gold standard." Operating characteristics of the search strategies were calculated. FINDINGS: Of the 1,383 articles about treatment, 506 (36.6%) met basic criteria for scientific merit and 127 (17.9%) of the 711 articles classified as a review met the criteria for systematic reviews. For locating sound treatment studies, a three-term strategy maximized sensitivity at 99.4% but with compromised specificity at 58.3%, and a two-term strategy maximized specificity at 98.5% but with compromised sensitivity at 52.0%. For detecting systematic reviews, a three-term strategy maximized sensitivity at 91.3% while keeping specificity high at 95.4%, and a single-term strategy maximized specificity at 99.6% but with compromised sensitivity at 42.5%. Three-term search strategies optimizing sensitivity and specificity achieved these values over 91% for detecting sound treatment studies and over 76% for detecting systematic reviews. CONCLUSIONS: Search strategies combining indexing terms and text words can achieve high sensitivity and specificity for retrieving sound treatment studies and review articles in 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.
How this classification was reachedexpand
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 | 0.116 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".