Development and validation of LGBTQIA+ search filters: Report on process and pilot filter for queer women
Bibliographic record
Abstract
Introduction: A search filter for studies involving lesbian, gay, bisexual, transgender, queer, intersex, asexual, and additional sexual minority and gender identities (LGBTQIA+) populations has been developed and validated; however, the filter contained very small gold standard sets for some populations, and terminology, controlled vocabulary, and database functionality has subsequently evolved. We therefore sought to update and re-test the search filters for these selected subgroups using larger gold standard sets. We report on the development and validation of two versions of a sensitivity-maximizing search filter for queer women, including but not limited to lesbians and women who have sex with women (WSW). Methods: We developed a PubMed search filter for queer women using the relative recall approach and incorporating input from queer women. We tested different search combinations against the gold standard set; combinations were tested until a search with 100% sensitivity was identified. Results: We developed and tested variations of the search and now present two versions of the strategy with 99% and 100% sensitivity. The strategies included additional terms to improve sensitivity and proximity searching to improve recall and precision. Conclusions: The queer women search filters balance sensitivity and precision to facilitate comprehensive retrieval of studies involving queer women. The filters will require ongoing updates to adapt to evolving language and search platform functionalities. Strengths of the study include the involvement of the population of interest at each stage of the project. Future research will include development and testing of search filters for other LGBTQIA+ subgroups such as bisexual and transgender people.
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 | 0.159 | 0.369 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".