Stigma and healthcare access among transgender and gender‐diverse people: A qualitative meta‐synthesis
Bibliographic record
Abstract
AIM: To describe the experience of stigma among transgender and gender-diverse (TGD) people accessing care. DESIGN: A qualitative meta-synthesis. DATA SOURCES: CINAHL, Medline, PubMed and PsycINFO were used to retrieve English-language, peer-reviewed qualitative studies from September 2016 to September 2021. REVIEW METHODS: The methods for this qualitative meta-synthesis included four steps. Step 1: Form the clinical question and purpose of synthesis to define and refine search terms. Step 2: Evaluate the literature using PRISMA. Step 3: Appraise the literature using Lett's Critical Review Form. Step 4: Conduct a reciprocal translation to synthesize the results. RESULTS: Of the 12 articles that met the inclusion criteria, 3 were from nursing journals. The articles included were studies conducted in U.S.A., Brazil, Canada, Colombia, Mozambique, Sweden, Uganda and United Kingdom, and most recruited transfeminine-identifying participants. Three main themes emerged: stigma experienced within the individual, interpersonal and structural socio-ecological levels among TGD people accessing care. CONCLUSION: Stigma is a significant public health issue as it limits healthcare access among TGD people. Stigma experienced at various socio-ecological levels contributes to health disparities. Nurses must create and implement interventions informed by the lived experiences of TGD people. IMPACT: Extant literature on this topic is limited in the nursing literature. This qualitative meta-synthesis illuminates TGD identity stigma-related experiences using a socio-ecological framework. While stigma is often experienced at the individual and interpersonal levels, oppressive systems that value cisgender cultural norms over other gender identities allow structural-level stigma to occur. Nurses are morally, ethically and professionally responsible for challenging systems that create and sustain stigma. Understanding how stigma is experienced when accessing care can help deliver critical information needed to improve healthcare access among TGD people and other historically oppressed communities.
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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 | 0.057 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".