University students’ access and use of sexual and reproductive health services in Australia
Bibliographic record
Abstract
University attendance can lead to changes in sexual behaviour that place people at increased risk of negative sexual and reproductive health (SRH) experiences; however, relatively few studies have explored access to and use of SRH services by university students in Australia. A convenience sample of students (N = 4291) from five universities completed an online survey to examine barriers and facilitators to accessing SRH services. Descriptive statistics were used to analyse variations by gender, sexuality, and enrolment status. Content analyses were conducted on responses to two open-ended questions. Over half (59%, 2934) had never spoken to a health professional about SRH issues. Of the 41% (1357) who had, female, non-binary/gender diverse, and Australian-born students were significantly more likely to have done so than students identifying as male, heterosexual, or as an international student. Frequently reported barriers to use of SRH services were ‘feeling embarrassed’, ‘being judged’ and ‘not knowing where to go’. LGBTQIA+ students along with international and overseas-born domestic students faced specific access barriers. Recommendations targeting student-identified barriers to access are offered, calling for universities to support student wellbeing by providing supportive, non-judgemental, and inclusive SRH promotion and health services that cater to needs of diverse student cohorts.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".