Fertility treatments and the young women who use them: an Australian cohort study
Bibliographic record
Abstract
BACKGROUND: In Australia, fertility treatment is partly or wholly reimbursable under federal benefits schemes, without restrictions on age, number of treatment cycles or existing family size. In this study, we aimed to characterize the potential need for and use of fertility treatments in a population-based cohort of young Australian women. METHODS: We conducted structured interviews with 974 members of a cohort constructed by tracing all female infants born at a single general hospital in Adelaide between 1973 and 1975. The main outcome measures were pregnancy history, difficulty becoming pregnant and assistance sought to become pregnant. RESULTS: Of 657 women aged 30-32 who had sought pregnancy, 24% reported difficulty becoming pregnant and 26% had lost at least one pregnancy. Ovulatory problems (16%) and male fertility problems (13%) were common among those with difficulty. Over half of the women who had difficulty conceiving (58%) sought assistance, largely from specialists (53%). Consultations, tests and education only were common (22%), as were IVF/ICSI (17%). Close to a third (28%) of those seeking assistance were treated only with clomiphene, as were two-thirds (67%) of women with ovulatory problems. CONCLUSIONS: In this study, almost a quarter of women in their early 30s reported difficulty conceiving, and over a quarter reported pregnancy loss. This suggests that a significant proportion of young women experience substantial difficulties becoming pregnant. Our findings highlight the need to continue to document the range of women's reproductive experiences and to monitor fertility and treatment-seeking trends.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".