Redefining perinatal experience: A philosophical exploration of a hypothetical case of gender diversity in labour and birth
Bibliographic record
Abstract
OBJECTIVE AND AIMS: Using a queer phenomenological approach, the objective of this philosophical analysis was to explore the transgender experience in highly gendered clinical areas, such as the birth unit, and make recommendations on how to provide perinatal care that is inclusive of gender diversity within these areas. This study aims to describe a hypothetical clinical experience to provide insight on the institutional barriers that currently exist and to provide nurses and midwives with pragmatic strategies to enhance gender-diverse care in general and gendered clinical areas. BACKGROUND: Currently, general healthcare providers are not sufficiently educated on how to care for and meet the needs of people who identify as lesbian, gay, bisexual, trans, queer or questioning and other communities (LGBTQ+). This vulnerable population continually faces stigma, discrimination and marginalisation, which act as barriers to accessing healthcare services. Although transgender people often have difficulty accessing health care in general settings, they experience an even greater challenge within traditionally gendered clinical care areas. DESIGN: Queer phenomenology was used to guide a critical philosophical analysis of hypothetical case reflecting a clinical scenario regarding a transgender man's experience in labour and birth. DISCUSSION: Healthcare professionals often provide insufficient care to transgender persons, inadvertently leading to further marginalisation of this vulnerable population. Special consideration to provide gender-diverse care throughout the perinatal period is needed. Structures and supports are essential to enhance the care from providers in attending to the unique needs of transgender individuals and reduce oppressive effects from heteronormative environments. RELEVANCE TO CLINICAL PRACTICE: Nurses and midwives are leading exemplars of providing person-centred care and are capable of advocating for equitable care amongst all populations to influence systemic change. Strategies for implementing changes that address LGBTQ+ health needs and specific recommendations for providing gender-diverse care in the perinatal settings are discussed.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.043 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".