Barriers to Managing Fertility: Findings From the Understanding Fertility Management in Contemporary Australia Facebook Discussion Group
Bibliographic record
Abstract
BACKGROUND: As part of research investigating the complexities of managing fertility in Australia, public opinions about how Australians manage their fertility were sought from women and men. OBJECTIVE: To identify public opinion about sexual and reproductive health in Australia. METHODS: To ensure access to a diverse group of people throughout Australia, an online group was advertised and convened on Facebook from October through December 2013. In a closed-group moderated discussion, participants responded to questions about how people in Australia attempt to manage three aspects of fertility: avoiding pregnancy, achieving pregnancy, and difficulties conceiving. Nonidentifiable demographic information was sought; no personal accounts of fertility management were requested. The discussion transcript was analyzed thematically. RESULTS: There were 61 female and 2 male Facebook users aged 18 to 50 years living in Australia participating in the study. Four main themes about fertility management were identified: access, geographical location, knowledge, and cost. Participants reported that young people and people from rural areas face barriers accessing contraception and fertility services. Limited knowledge about sex and reproduction and the cost of fertility services and contraception were also said to impede effective fertility management. CONCLUSIONS: Reasons for inequalities in effective fertility management that are amenable to change were identified. Facebook is an effective method for gaining insights into public opinion about sexual and reproductive health.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".