Optimal search strategies for identifying mental health content in MEDLINE: an analytic survey
Bibliographic record
Abstract
OBJECTIVE: General practitioners, mental health practitioners, and researchers wishing to retrieve the best current research evidence in the content area of mental health may have a difficult time when searching large electronic databases such as MEDLINE. When MEDLINE is searched unaided, key articles are often missed while retrieving many articles that are irrelevant to the search. The objectives of this study were to develop optimal search strategies to detect articles with mental health content and to determine the effect of combining mental health content search strategies with methodologic search strategies calibrated to detect the best studies of treatment. METHOD: An analytic survey was conducted, comparing hand searches of 29 journals with retrievals from MEDLINE for 3,395 candidate search terms and 11,317 combinations. The sensitivity, specificity, precision, and accuracy of the search strategies were calculated. RESULTS: 3,277 (26.8%) of the 12,233 articles classified in the 29 journals were considered to be of interest to the discipline area of mental health. Search term combinations reached peak sensitivities of 98.4% with specificity at 50.0%, whereas combinations of search terms to optimize specificity reached peak specificities of 97.1% with sensitivity at 51.7%. Combining content search strategies with methodologic search strategies for treatment led to improved precision: substantive decreases in the number of articles that needed to be sorted through in order to find target articles. CONCLUSION: Empirically derived search strategies can achieve high sensitivity and specificity for retrieving mental health content from MEDLINE. Combining content search strategies with methodologic search strategies led to more precise searches.
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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 | 0.126 | 0.520 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".