Advancing the conversation: next steps for lesbian, gay, bisexual, trans, and queer (LGBTQ) health sciences librarianship
Bibliographic record
Abstract
In recent years, librarians in various sectors have been moving forward a conversation on the distinct information needs and information-seeking behavior of our lesbian, gay, bisexual, trans, and queer (LGBTQ) patrons and how well the profession recognizes and meets those needs. Health sciences librarianship has been slower than other areas of the profession in creating an evidence base covering the needs of its LGBTQ patrons, with, until recently, only very limited literature on this subject. LGBTQ health sciences librarianship is now starting to attract new interest, with librarians working together to bring this emerging specialization to the attention of the broader professional community. In this paper, the authors report on a dedicated panel discussion that took place at the 2016 joint annual meeting of the Medical Library Association and Canadian Health Libraries Association/Association des bibliothèques de la santé du Canada in Toronto, Ontario, Canada; discuss subsequent reflections; and highlight the emerging role for health sciences librarians in providing culturally competent services to the LGBTQ population. Recommendations are also provided for establishing a tool kit for LGBTQ health sciences librarianship from which librarians can draw. We conclude by highlighting the importance of critically reflective practice in health sciences librarianship in the context of LGBTQ health information.
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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 | 0.107 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.067 | 0.068 |
| Scholarly communication | 0.052 | 0.044 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.025 | 0.037 |
| Insufficient payload (model declined to judge) | 0.014 | 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".