Advancing Reproductive Health through Policy-Engaged Research in Abortion Care
Bibliographic record
Abstract
Mifepristone medication abortion was first approved in China and France more than 30 years ago and is now used in more than 60 countries worldwide. It is a highly safe and effective method that has the potential to increase population access to abortion in early pregnancy, closer to home. In both Canada and the United States, the initial regulations for distribution, prescribing, and dispensing of mifepristone were highly restricted. However, in Canada, where mifepristone was made available in 2017, most restrictions on the medication were removed in the first year of its availability. The Canadian regulation of mifepristone as a normal prescription makes access possible in community primary care through a physician or nurse practitioner prescription, which any pharmacist can dispense. In this approach, people decide when and where to take their medication. We explore how policy-maker-engaged research advanced reproductive health policy and facilitated this rapid change in Canada. We discuss the implications of these policy advances for self-management of abortion and demonstrate how in Canada patients "self-manage" components of the abortion process within a supportive health care system.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".