Fertility Knowledge Among Women Struggling to Conceive Without Medical Intervention: A Brief Report
Bibliographic record
Abstract
Introduction Approximately 1 in 6 women experience infertility. Though medical treatments for infertility exist, they are very costly and highly burdensome for women. It is therefore desirable to optimize women's chances of conception without medical intervention by ensuring that they have adequate knowledge of the female menstrual cycle and the timing of the fertile window. The current study therefore aimed to assess the degree to which women struggling to conceive without medical intervention are knowledgeable about these topics. Methods One hundred and two women of reproductive age (18–45 years old) in Canada and the United States who had been struggling to conceive without medical intervention for ≥12 months completed an online survey including a questionnaire assessing knowledge related to reproduction and fertility. Results Mean accuracy score on the Fertility Knowledge Questionnaire was 67%. Seventy-two women were not aware that the week before ovulation was associated with the highest chances of conception. Women using cervical mucus tracking to increase chances of conception were more knowledgeable ( p = 0.02), as were women with more formal education ( p = 0.01). Conversely, women who had been attempting to conceive for longer had lower fertility knowledge ( p = 0.03). Age, number of children, and family income were unrelated to fertility knowledge ( p > 0.05). Discussion Our findings suggest that women who are struggling to conceive would benefit from education related to the timing and identification of the fertile window. Reproductive and primary healthcare providers can play an important role in assessing fertility knowledge and addressing knowledge gaps to improve chances of successful conception.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".