Legal and ethical standards for protecting women's human rights and the practice of conscientious objection in reproductive healthcare settings
Bibliographic record
Abstract
The practice of conscientious objection by healthcare workers is growing across the globe. It is most common in reproductive healthcare settings because of the religious or moral values placed on beliefs as to when life begins. It is often invoked in the context of abortion and contraceptive services, including the provision of information related to such services. Few states adequately regulate the practice, leading to denial of access to lawful reproductive healthcare services and violations of fundamental human rights. International ethical, health, and human rights standards have recently attempted to address these challenges by harmonizing the practice of conscientious objection with women's right to sexual and reproductive health services. FIGO ethical standards have had an important role in influencing human rights development in this area. They consider regulation of the unfettered use of conscientious objection essential to the realization of sexual and reproductive rights. Under international human rights law, states have a positive obligation to act in this regard. While ethical and human rights standards regarding this issue are growing, they do not yet exhaustively cover all the situations in which women's health and human rights are in jeopardy because of the practice. The present article sets forth existing ethical and human rights standards on the issue and illustrates the need for further development and clarity on balancing these rights and interests.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".