Comparing fertility clinic care for individuals and couples living with and affected by HIV and in Canada from 2014 to 2023
Bibliographic record
Abstract
Access to fertility care, particularly medically assisted conception (MAC), is a reproductive justice concern for individuals with HIV. This longitudinal study investigates MAC access for people with HIV in Canada. Surveys were distributed to medical and laboratory directors from 58 fertility clinics across nine provinces. Responses were initiated by 24/89 (27.0%) participants, representing 19/58 (32.8%) clinics in 7 provinces. Complete responses were received from 16/24 (66.7%) participants. Of the 14 clinics, 8 (57.1%) reported they will see individuals with HIV in consultation, with an additional 2 (10.5%) limiting this to people with an undetectable viral load. Respondents from 11 clinics, who completed the survey, answered questions about access to MAC. 9/11 (81.8%) offer intrauterine insemination if the viral load is undetectable, while 8/11 (62.5%) offer in vitro fertilization (IVF) under the same condition. Only one (9.1%) offers IVF regardless of viral load, 18.2% (2/11) do not offer IVF in the context of HIV. Three-quarters of the respondents were aware of Canadian guidelines related to HIV. Among those adopting guideline recommendations, 75% found them helpful. While the study's low response limits the generalizability, findings suggest that advocacy efforts are warranted to address reproductive rights for people with HIV, emphasizing U = U.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".