LGBTQ+ Health Research Guides: A Cross-institutional Pilot Study of Usage Patterns
Bibliographic record
Abstract
Objectives: Multiple authors have recommended that health sciences libraries use research guides to promote LGBTQ+ health information, connect with their users and the community, and improve health equity. However, little is known about LGBTQ+ health guide usage patterns and whether such guides really meet the information needs of their users. Based on usage patterns from LGBTQ+ health research guides, we assessed the types of LGBTQ+ health information of greatest interest to health sciences library users and how, if appropriate, these guides might be revised to be more relevant to user needs. Methods: The data for LGBTQ+ health research guides of five health sciences libraries (three in the United States and two in Canada) were studied. Usage data were retrieved for a three year period (July 2018-June 2021). Two separate factors were chosen for analysis: monthly guide usage over time and the individual types of resources used. Monthly usage was studied by generating line graphs in Excel with trendlines to calculate overall guide usage trends. To determine the most sought-after types of resources by users, clicks for individual resources were categorized by type and focus using open coding in Google Sheets. Results: Overall guide usage was mixed, with some libraries’ guides trending upward over time and others downward. Analysis of the resource links showed that links to local and community health resources were among the most heavily clicked (64.11% of clicks), as were resources designed to help patients find healthcare providers and services (53.23%). Links to library-owned resources, such as books, journals, and databases, were generally clicked less (2.44%), as were links aimed at healthcare professionals (11.36%). Conclusions: The usage statistics for the guides were relatively low. However, the size of the LGBTQ+ community is relatively low compared to the general population and therefore LGBTQ+ health can be considered a category of minority health. We argue that the importance of providing quality LGBTQ+ health information outweighs any concerns of large-scale usage, and that providing such guides promotes health equity. The higher usage numbers for local resources supports the idea that guides are most useful when they link users to services and providers in their own communities. This suggests a best practice for librarians to focus on local resources and collaborations, and on consumer health resources, when creating and editing these 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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".