L’accès aux services de fertilité pour les femmes lesbiennes, bisexuelles ou pansexuelles et les personnes queer, trans ou non-binaires : une revue rapide des écrits scientifiques
Bibliographic record
Abstract
Introduction: Lesbian, bisexual, and pansexual (LBP) women as well as transgender, queer, and non-binary individuals (TQNB) often rely on medically assisted reproduction (MAR) to build their families, but do not always have access to these services. Currently, there appears to be no literature that comprehensively reviews, from an ecosystemic perspective, the main factors determining LBP women's and TQNB people's access to MAR. Objective: This rapid review aims to identify, from an ecosystemic perspective, the factors described in the scientific literature as influencing LBP women's and TQNB people's access to MAR. Methods: A literature search using 11 search engines identified 22 articles presenting results of recent empirical studies (2018-2023) using various methodologies. Relevant results were subjected to thematic analysis, and identified factors were classified within an ecosystemic model. Results: The identified factors are (1) at the microsystemic level, support from the social network and healthcare personnel; (2) at the exosystemic level, healthcare personnel's awareness of sexual and gender diversity, adequacy of information and documentation, organizational logic of fertility services, cost of services, legislation; (3) at the macrosystemic level, heterocisnormativity as a cross-cutting influence. Discussion and conclusion: To ensure access to MAR for LBP women and TQNB people, a comprehensive and multi-level approach is necessary. Suggestions for health practices and public policies are proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".