Toward a Public Health Approach to Infertility: The Ethical Dimensions of Infertility Prevention
Bibliographic record
Abstract
While many experts and organizations have recognized infertility as a public health issue, most governments have not yet adopted a public health approach to infertility. This article argues in favor of such an approach by discussing the various implications of infertility for public health. We use a conceptual framework that focuses on the dual meaning of the term ‘public’ in this context: the health of the public, as opposed to that of individuals, and the public/collective nature of the required interventions. This analysis highlights the need for a comprehensive public health approach toward infertility, points to some initiatives that are already in place and demonstrates that prevention is currently a neglected—yet much needed—element. We move on to discuss the sensitive nature of prevention initiatives as a probable explanation for their scarcity. We illustrate the complexity of prevention through an analysis of an infertility prevention campaign previously conducted in the United States, which provoked significant controversy. We use a public health communication ethics framework to expose the strengths and the shortcomings of this campaign, and conclude that prevention initiatives targeting infertility can indeed be conducted in a sensible way that promotes autonomy while improving public health.
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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 | 0.053 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.088 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.014 | 0.018 |
| 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".