The preferences of women in Australia for the features of long-acting reversible contraception: results of a discrete choice experiment
Bibliographic record
Abstract
PURPOSE: Long-acting reversible contraceptives (LARC), such as intrauterine devices (IUD) and implants, are highly effective. However, the uptake of LARC in Australia has been slow and the oral contraceptive pill (OC) remains the best known and most widely used contraceptive. Our aim was to investigate women's preferences for the features of LARC. METHODS: We used a discrete choice experiment (DCE) in which each respondent completed 12 choice tasks. We recruited a general population sample of 621 women in Australia aged 18-49 using an online survey panel. A mixed logit model was used to analyse DCE responses; a latent class model explored preference heterogeneity. RESULTS: Overall, 391 (63%) of women were currently using contraception; 49.3% were using an OC. About 22% of women were using a LARC. Women prefer products that are more effective in preventing pregnancy, have low levels of adverse events (including negative effects on mood), and which their general practitioner (GP) recommends or says is suitable for them. CONCLUSIONS: Women have strong preferences for contraceptive products that are effective, safe, and recommended by their GP. The results indicate which characteristics of LARCs need to be front and centre in information material and in discussions between women and healthcare professionals.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".