LGBTQ+ health research guides at North American health sciences libraries: a survey and content analysis
Bibliographic record
Abstract
OBJECTIVES: Current literature recommends online research guides as an easy and effective tool to promote LGBTQ+ health information to both health care providers and the public. This cross-sectional study was designed to determine how extensive LGBTQ+ health guides are among hospital and academic libraries and which features are most prevalent. METHODS: In order to locate LGBTQ+ health guides for content analysis, we searched for guides on the websites of libraries belonging to the Association of Academic Health Sciences Libraries (AAHSL) and the Canadian Association of Research Libraries (CARL). Additionally, we searched the Springshare interface for LibGuides with the word "health" and either "LGBT" or "transgender." Content analysis was performed to identify major characteristics of the located guides, including target audience and the information type provided. RESULTS: LGBTQ+ research guides were identified for 74 libraries. Of these, 5 were hospital libraries, and the rest were academic libraries. Of 158 AAHSL member libraries, 48 (30.4%) had LGBTQ+ guides on their websites. Nearly all guides (95.9%) provided general LGBTQ+ health information, and a large majority (87.8%) also had information resources for transgender health. Smaller percentages of guides contained information on HIV/AIDS (48.6%) and women's health (16.2%). CONCLUSIONS: Even though literature recommends creating LGBTQ+ health guides, most health sciences libraries are missing an opportunity by not developing and maintaining these guides. Further research may be needed to determine the usage and usefulness of existing guides and to better identify barriers preventing libraries from creating guides.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".