Public Health Association of Australia: Policy-at-a-glance – Abortion Policy
Bibliographic record
Abstract
increased education, fertility awareness, uptake of effective contraception and respectful relationships. This may be assisted through a comprehensive national sexual and reproductive health strategy. 2. While the primary public health goal in the area of unintended pregnancy is prevention, even with good prevention strategies abortion services will always be needed. 3. Abortion is a common part of many women’s reproductive experience with one quarter to one third of all Australian women having an abortion at some point in their life. 4. In the Australian setting, abortion is an extremely safe procedure. Internationally, access to safe, legal abortion significantly reduces maternal mortality. 5. Abortion should be regulated in the same way as other health procedures, without additional barriers or conditions. Regulation of abortion should be removed from Australian criminal law. 6. States and territories should actively work toward equitable access (including geographic and financial access) to abortion services, with a mix of public and private services available. Summary: Abortion is a safe, common medical procedure which should be regulated in the same way as other medical procedures. Both medical and surgical abortion should be included in health service planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.019 | 0.012 |
| Insufficient payload (model declined to judge) | 0.073 | 0.017 |
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".