Let's talk about sex: A feminist poststructural approach to addressing sexual health in the healthcare setting
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To explore the use of feminist poststructuralism (FPS) as a way to critique, understand and improve sexual health care and policy in healthcare settings. BACKGROUND: Sexual health is an important aspect of health; however, in healthcare settings, it often goes unaddressed by both healthcare providers and patients due to stigma, taboo, fear of embarrassment or uncertainty. Lack of attention to sexual health has been stated as a legitimate concern for patients across the lifespan; there remain gaps in implementing sexual health care discussions into practice in healthcare settings. DESIGN: A critical analysis will be presented to explore sexual health care and attitudes in the healthcare setting from patient and nursing perspectives using FPS. METHODS: Feminist poststructuralism is used to examine the meaning of experience that is personally, socially and institutionally constructed through relations of power. FPS will also be applied to understand how sexual health discourses are negotiated in healthcare settings. SQUIRE guidelines were used in the preparation of this paper (See Appendix S1). RELEVANCE TO CLINICAL PRACTICE: The application of a feminist poststructural lens to sexual health care in healthcare settings may be used by healthcare professionals to understand, question and challenge how social and institutional beliefs, values and practices surrounding sexual health, inclusive of a patient's sexual pleasure or sexual activity, are experienced by healthcare professionals and patients. This theoretical and methodological approach could lead to identifying possibilities for change in healthcare settings that are inclusive and supportive of sexual health 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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.052 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".