Locating sex- and gender-specific data in health promotion research: evaluating the sensitivity and precision of published filters
Bibliographic record
Abstract
OBJECTIVE: This study explored the effectiveness of search filters in identifying sex- and gender-specific data in health promotion studies that are indexed in MEDLINE. METHODS: Literature searches were conducted to identify studies on patient or consumer attitudes and behaviors toward colorectal cancer screening, nutritional labeling, and influenza vaccination. Publications reporting sex- or gender-specific outcome data constituted the gold standards for this study. The sensitivity and precision of previously published gender-specific filters, as well as individual filter component terms, were calculated and compared with values identified in prior studies. RESULTS: The sensitivity and precision of published sex or gender filters varied across topics. Sensitivity values ranged from 14.3% to 92.5%, while precision varied from 17.9% to 51.4%. These filters were less sensitive and less precise in their identification of relevant studies than has been reported in previous studies. Further, while the MEDLINE Medical Subject Headings (MeSH) term "Sex Factors" achieved the greatest average precision (59.3%) of any individual filter term, the MEDLINE check tag "Female" returned the highest average sensitivity (90.1%), with an average precision of 25.0% across topics. CONCLUSIONS: Although search filters can facilitate the identification of research evidence to enable decision making, variability in study abstracting and indexing can limit the generalizability and usability of these filters. This potential for variability should be considered when deciding to incorporate a search filter into any literature search. This research highlights the importance of this awareness when developing strategies for searching the published literature and the potential value of supplementing database searching with other methods of study identification.
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 | 0.681 | 0.894 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.048 | 0.027 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.007 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".