Physician Attitudes Regarding Pregnancy, Fertility Care, and Assisted Reproductive Technologies for HIV-Infected Individuals and Couples
Bibliographic record
Abstract
BACKGROUND: Family and pregnancy planning are important for HIV-infected individuals and couples. There is a paucity of data regarding physician attitudes with respect to reproduction in this population, but some evidence suggests that attitudes can influence the information, advice, and services they will provide. OBJECTIVE: To determine physician attitudes toward pregnancy, fertility care, and access to assisted reproductive technologies for HIV-infected individuals, and to determine whether attitudes differed based on specific physician characteristics. METHODS: A survey was sent electronically to obstetrician/gynecologists and infectious disease specialists in Canada. Items were grouped into 5 key domains: physician demographics, physician attitudes toward pregnancy and adoption, physician attitudes toward fertility care, physician attitudes toward assisted reproductive technology, and challenges for an HIV-infected population. Attitudes were determined based on answers to individual questions and also for each domain. Univariate and logistic regression analyses were used to determine the influence of specific physician characteristics on attitudes. RESULTS: Completed surveys were received from 165 physicians. Most had positive attitudes regarding pregnancy or adoption (89%), fertility care (72%), and assisted reproductive technology (79%). In multivariate analyses, having cared for HIV-infected patients was significantly associated with having a positive attitude toward fertility care or assisted reproductive technology. CONCLUSIONS: In this national survey of Canadian physicians, most had positive attitudes toward pregnancy, adoption, fertility care, and use of assisted reproductive technology among HIV-infected persons. Physicians who had cared for HIV-infected individuals in the past were more likely to have positive attitudes than those who had not.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".