The process of transitioning for the transgender individual and the nursing imperative: A narrative review
Bibliographic record
Abstract
AIMS: The objectives of this study were to bring the experience of the transitioning process for the transgender population to the nursing profession and address the lack of knowledge to promote improved patient outcomes. DESIGN: This study used a narrative review using the literature matrix method. Because of the dearth of trans specific literature, editorials and monologues were included. DATA SOURCE: A broad search was undertaken across all databases including CINAHL, PubMed, PsycINFO, Ovid MEDLINE, ProQuest Nursing & Allied Health and Google Scholar. Literature from June 1994 to May 2020 was appraised. Non-peer reviewed literature and published texts were procured via Google Alerts. REVIEW METHODS: Selection for inclusion was based on credibility and relevance from a variety of social science disciplines. A narrative analysis was used to identify common themes, incongruencies in schools of thought and perspectives that require consideration. RESULTS: Analysis of the literature revealed the following themes: (a) literature and terminology evolution, (b) transitioning as a process, (c) medicalization of transitioning, (d) generational views on transitioning and (e) needs during transition. CONCLUSION: This review highlights key issues about the transitioning process imperative to nursing when meeting the needs of the transgender population. IMPACT: This review addresses the lack of trans specific literature and lack of consistency in the literature about the understanding of the transitioning process for the transgender population. Main findings? Terminology to explain the transitioning process is ever evolving. Future studies about transitioning need to go beyond the medical lens. Generational views differ in the approach to transitioning, and there are needs unique to this population required during the process. Where and whom will the research impact? The review has significant implications for change in health delivery, nursing policy and formulating nursing practice and education to improve trans competent care.
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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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".