Sociodemographic characteristics associated with the use of effective and less effective contraceptive methods: findings from the Understanding Fertility Management in Contemporary Australia survey
Bibliographic record
Abstract
OBJECTIVE: Unintended pregnancy and abortion may, in part, result from suboptimal use of effective contraception. This study aimed to identify sociodemographic factors associated with the use of effective and less effective methods among women and men of reproductive age living in Australia. METHODS: In a cross-sectional national survey, 1544 women and men aged 18-51 were identified as being at risk of pregnancy. Chi-square and logistic regression analyses were used to assess the sociodemographic factors related to contraceptive use. RESULTS: Most respondents (n = 1307, 84.7%) reported using a method of contraception. Use of any contraceptive was associated with being born in Australia (Odds Ratio [OR] 1.89; 95% Confidence Interval [CI]1.186, 3.01; p = .008), having English as a first language (OR 1.81; 95% CI: 1.07, 3.04; p = .026), having private health insurance (OR 2.25; 95% CI 1.66, 3.04; p < .001), and not considering religion important to fertility choices (OR 0.43; 95%CI 0.31, 0.60; p < .001). A third used effective contraceptive methods (n = 534, 34.6%; permanent methods: 23.1%, and long-acting reversible contraception (LARC): 11.4%). Permanent methods were more likely to be used in rural areas (OR 0.62; 95%CI 0.46, 0.84; p = .002). Use of the least effective, short-term methods was reported by nearly half (condoms: 25.6%, withdrawal: 12.5%, and fertility-awareness-based methods: 2.8%). Those who relied on withdrawal were more likely to live in a metropolitan area (OR 2.85; 95% CI 1.95, 4.18; p < .001), and not have private health insurance (OR 0.52; 95% CI 0.38, 0.71; p < .001). CONCLUSIONS: Targeted promotion of the broad range of available contraceptives may raise awareness and uptake of more effective methods and improve reproductive autonomy in certain population groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".