LGBTQ+ health research guides: a multi-institutional analysis of usage patterns and user information needs
Bibliographic record
Abstract
Objective: LGBTQ+ health research guides can strengthen the LGBTQ+ community through connecting people to quality health services and information, and previous studies have recommended that health sciences libraries create and maintain these guides. Little evidence exists, though, on how these guides are used and how well they meet the needs of LGBTQ+ users. Using retrospective data retrieved from multiple LGBTQ+ health research guides, we examined the categories of LGBTQ+ health information most used, as well as how often guides were accessed. Based on these results, we hope to find patterns which can lead to best practices for libraries. Methods: Five North American academic health sciences libraries contributed select usage data from their LGBTQ+ health research guides, covering a three-year period (July 2018-June 2021). Data was analyzed in two ways. Firstly, the 20 most-clicked resources from each guide were categorized through open coding, to assess if certain information resource categories were more popular among guide users, allowing for inference of user needs. A time-series analysis was also conducted for two sites, using the Classical Seasonal Decomposition by Moving Averages method, to provide deeper insights into the data. Results: Open coding data showed consumer health information resources were used more often than other health resource categories. Resources from more locally based organizations and those with provider and services information were heavily used, indicating that users may be looking for information connecting to local health services and providers. The time series analysis allowed the potential positive effect of guide promotion to be showcased in ways that would not have been clear from the raw data. Conclusion: This study shows that people are accessing LGBTQ+ consumer health information through academic library research guides, with a preference for local information. Guide usage appears to be positively driven by outreach within one's institution and to the greater community. Locating external partners may increase guide impact and provide important links to local resources and services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".