Retrieving Transgender and Gender Diverse Literature: Protocol for the Development and Validation of 2 Search Hedges
Bibliographic record
Abstract
BACKGROUND: Searching for transgender and gender diverse (TGD) references within large academic databases can be a challenging process, partly due to the dynamic and diverse definitions of words and terminologies used by multiple interest holders. Search hedges are preestablished search strings that aid in the efficacy of identifying and screening relevant articles. Validated search hedges focused on TGD people and topics will aid in identifying relevant literature. OBJECTIVE: This study aims to develop and validate the sensitivity and precision of 2 interdisciplinary and cross-cultural TGD search hedges designed for retrieving references from MEDLINE and APA PsycInfo, both on the Ovid platform. METHODS: Searches were conducted using the finalized search hedges via Ovid on June 7, 2024, yielding 31,055 references from MEDLINE and 22,924 references from APA PsycInfo. A random sample of 2330 records from MEDLINE and 2293 records from APA PsycInfo will be independently screened by at least 2 team members. At the title and abstract screening stage, references will be excluded if they (1) use solely binary terminology to describe gender, (2) focus on psychometric measurement of gender, or (3) focus on intersex or differences of sex development (DSD) topics. References will be included if they (1) report on transgender or gender diverse people, or both, in their sample; or (2) specifically discuss TGD communities or TGD topics. References without an abstract will be categorized as No_Abstract. References in which the TGD population is unclear will be categorized as LGB_Maybe_T or Mixed_Topics. Only references in the No_Abstract, LGB_Maybe_T, or Mixed_Topics categories will proceed to the full-text screening phase. In the full-text screening phase, references will be categorized as included if they (1) clearly distinguish between sexual identity and gender identity, (2) mention or discuss TGD topics or experiences in the Methods or Results sections, (3) communicate consideration for participants' gender self-identification and experiences, or (4) consider TGD populations as a distinct subpopulation. The results of the screening process will be used to calculate precision and sensitivity, with a targeted sensitivity of 100% and a targeted precision of 76% for each search hedge. RESULTS: Validation and data analysis are projected to be finished by December 2025, with results expected to be published in 2026. CONCLUSIONS: Rigorous and transparent knowledge synthesis processes, starting with a high-quality search hedge, can help inform and equip community members, clinicians, policymakers, and other key decision-makers with scientifically sound evidence. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/76055.
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 | 0.173 | 0.226 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.084 | 0.025 |
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".