Australians’ understanding of the decline in fertility with increasing age and attitudes towards ovarian reserve screening
Bibliographic record
Abstract
The aim of this study is to determine Australians' understanding of the decline in fertility with age, social determinants that influence their decision to start a family and attitudes towards ovarian reserve screening as a tool allowing personalised reproductive life planning. An online survey of 383 childless Australian men and women, aged 18-45 years, was conducted. Both sexes overestimated natural and in vitro fertilization (IVF)-assisted fertility potential with increasing age, with the magnitude of overestimation being more pronounced for men and IVF treatment compared with natural conception. The primary determinants for starting a family were a stable relationship, followed by establishment of career; availability of accessible child care and paid parental leave were considered less important. Finally, the majority of women (74%) would alter their reproductive life planning if they were identified as having low ovarian reserve on screening. Despite increased education, Australians continue to have a poor understanding of age-related decline in natural and IVF-assisted conception, potentially explaining why many delay starting a family. Ovarian reserve screening may help identify individuals at increased risk of premature diminished fertility, giving these women the ability to bring forward their plans for natural conception or undertake fertility preservation (oocyte freezing).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".