Toward a Public Health Approach to Infertility: The Ethical Dimensions of Infertility Prevention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it