Determinants of Women's Decision Making on Whether to Treat Nausea and Vomiting of Pregnancy Pharmacologically
Bibliographic record
Abstract
Nausea and vomiting of pregnancy (NVP) affects up to 80% of all women to some degree during their pregnancies. Diclectin (doxylamine and pyridoxine [vitamin B6]) has been on the Canadian market for many years and is indicated as the drug of choice for the treatment of NVP. However, some women choose not to treat NVP with pharmacologic measures, perhaps due to a persistent fear of teratogenic risk. The objective of this study was to determine the factors that influence a woman's decision not to treat NVP with pharmacologic measures. Fifty-nine women recruited from the Motherisk Nausea and Vomiting Helpline completed a questionnaire. All were informed that Diclectin was considered safe for use during pregnancy. At a follow-up telephone call, 34% were not using any pharmacologic treatment, and of those who were taking the drug, 26% were using less than the recommended dose. Reasons cited for not using the medication were insufficient safety data, preference for non-pharmacologic methods, and being made to feel uncomfortable by the physician. Of the women who did use Diclectin, the most convincing reassuring information that it was safe to use came from friends and family. Many other factors play a large role in a women's decision making.
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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".