Transgender and Gender-Nonconforming Populations Experience Unique Challenges in Health Information Environment Developed for Heteronormative Audience
Bibliographic record
Abstract
A Review of: Tenny, C. S., Surkan, K. J., Gerido, L. H., & Betts-Green, D. (2021). A crisis of erasure: Transgender and gender-nonconforming populations navigating breast cancer health information. The International Journal of Information, Diversity, & Inclusion, 5(4), 132–149. https://doi.org/10.33137/ijidi.v5i4.37406 Objective – To understand the lived experiences of transgender and gender-nonconforming populations in seeking health information about breast cancer. Design – Thematic literature review. Setting – Four English-language databases featuring clinical, patient engagement, and library and information sciences (LIS) research. Subjects – Twenty-one published articles. Methods – The researchers chose three concepts (trans, LGBTQ+, and breast cancer), identified related terms for each, and used these terms to conduct literature searches in four databases: PubMed, Web of Science, Library Literature & Information Science Full Text, and Library, Information, Science & Technology Abstracts. Search results were reviewed for relevance to the research objective. The researchers applied grounded theory to analyze the 21 selected articles through open, axial, and selective (thematic) coding. The qualitative research software NVivo was used to perform thematic analysis of each article, and a shared codebook was developed to ensure saturation of axial themes and consistency of coding amongst researchers. Main Results – Three overarching themes emerged from selective coding that exemplify experiences of transgender and gender-nonconforming persons seeking health information about breast cancer: access, erasure, and quality. Compared to their cisgender peers, these historically marginalized populations and their caregivers experience more difficulty accessing the already limited breast cancer information, healthcare, and support services suited to their needs. In particular, transgender and gender-nonconforming patients are often burdened with choosing between receiving health information and care designed for heteronormative persons and risking self-disclosure and possible discrimination by culturally incompetent health professionals. Conclusion – The researchers noted the alarmingly limited resources available for gender-nonconforming patients seeking information and support for health matters other than mental health or sexually transmitted diseases. The researchers also called for increased efforts by LIS curriculums and professionals to study and understand the needs of transgender and gender-nonconforming patrons, and to improve the quality and quantity of information resources specifically dedicated to these unique populations.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".