Bibliographic record
Abstract
Increasing awareness of healthcare disparities and unique health needs of LGBTQ2S people calls for a revitalization of health professional training programs.As new topics become integrated into these programs, there is a great need for a comprehensive resource that aligns with Canadian guidelines and standards of care.Caring for LGBTQ2S People identifies gaps in care and health care disparities, and provides clinicians with both the knowledge and the tools to continue to improve the health of LGBTQ2S people.Written by expert authors, this fully updated version builds on the critically praised first edition and highlights the significant social, medical, and legal progress that has occurred in Canada since 2003.The book includes general medical information and guidance that is useful for anyone providing care to LGBTQ2S people.Chapters in this edition provide background on the fundamentals of language, cultural competency, and the patient-provider relationship, and include contemporary and expanded discussion on STIs, HIV, substance use, mental health, fertility, and trans health.This clinical guide is written for a general and trainee-level reader in health care and primary care and showcases a comprehensive understanding of LGBTQ2S health while also concluding with unique considerations for those who experience an intersection of diverse identities.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.892 | 0.842 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".