A Review of the State of HIV Nursing Science With Sexual Orientation, Gender Identity/Expression Peoples
Bibliographic record
Abstract
ABSTRACT: Throughout the HIV pandemic, nurses have contributed to or led approaches to understanding the effects of HIV disease at individual and societal levels. Nurses have advocated for socially just care for more than a century, and our efforts have created a foundation on which to further build the state of HIV nursing science with sexual orientation and gender identity/expression (SOGI) Peoples. Nurses have also participated in the development of approaches to manage HIV disease for and in collaboration with populations directly affected by the disease. Our inclusive approach was guided by an international human rights legal framework to review the state of nursing science in HIV with SOGI Peoples. We identified articles that provide practice guidance (n = 44) and interventions (n = 26) to address the health concerns of SOGI Peoples and our communities. Practice guidance articles were categorized by SOGI group: SOGI People collectively, bisexual, transgender, cisgender lesbian, women who have sex with women, cisgender gay men, and men who have sex with men. Interventions were categorized by societal level (i.e., individual, family, and structural). Our review revealed opportunities for future HIV nursing science and practices that are inclusive of SOGI Peoples. Through integrated collaborative efforts, nurses can help SOGI communities achieve optimal health outcomes that are based on dignity and respect for human rights.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".