Bibliographic record
Abstract
During her second week in practice, Larisa encountered a 17-year-old woman, Zoë, who had decided she wanted to have an abortion. The teen chose Larisa over the older partners in the practice as she thought the young doctor might be more supportive of her decision. During medical school, Larisa always thought she would be a family physician who would offer medication abortion to her patients as part of her general primary care practice. She enjoyed an opportunity to see abortions covered by national health insurance in Canada during an elective rotation in women’s health in Toronto. But she became caught up learning so many other things during residency and never found a mentor to help her figure out how to integrate abortion care into her practice. In her new practice, Larisa learned quickly about the problems that women faced in her community around reproductive choice. Her practice was in a conservative county where the school board had consistently opposed sex education in the schools. The area was 50 miles from a metropolitan area where abortion was available, in a state with strict laws about teenagers and parental consent and with a state legislature considering even stricter regulations about abortion in the future. Larisa learned that teenagers were concerned about confidentiality in obtaining birth control at local pharmacies, had trouble getting to the city for birth control or abortion services, and lacked solid understanding of their contraceptive choices. Uninsured patients were unable to afford either medication or surgery for abortion and had few options. Larisa spent a long time trying to figure out how to help Zoë with the unwanted pregnancy. Although she could not yet offer the service in her practice, she was able to share what she did know about abortion with Zoë.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".