Bibliographic record
Abstract
Although women with serious mental illness have high rates of lifetime sexual partners, they infrequently use contraception. Consequently, the prevalence of sexually transmitted infections is high in this population. In addition, while the overall rate of pregnancy in women with schizophrenia of child-bearing age is lower than in the general population, the percentage of pregnancies that are unwanted is higher than that in the general population. The objective of this paper is to help clinicians explore knowledge of appropriate methods of contraception for women who suffer from schizophrenia. The authors reviewed recent literature on the use of contraceptive methods by women with schizophrenia treated with antipsychotic and adjunctive medications. Contraceptive counseling to women and their partners is an important part of comprehensive care for women with serious and persistent mental illness. Women with schizophrenia who smoke, are overweight, or have diabetes, migraine, cardiovascular disease, or a family history of breast cancer should be offered non-hormonal contraception. Women with more than one sexual partner should be advised on barrier methods in addition to any other contraceptive measures they are using. Clinicians should be alert for potential interactions among oral hormonal contraceptives, smoking, and therapeutic drugs. Long-lasting contraceptive methods, such as intrauterine devices, progesterone depot injections, or tubal ligation are reasonable options for women having no wish to further expand their families.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".