MotherNature: Establishing a Canadian Research Network for Natural Health Products (NHPs) During Pregnancy and Lactation
Bibliographic record
Abstract
BACKGROUND: It has been estimated that between 7% and 55% of expectant mothers use herbal medicines or other types of natural health products (NHPs). Unfortunately, the safety and efficacy of NHPs during pregnancy and lactation is largely unknown. The Motherisk Program, at the Hospital for Sick Children, Toronto, Ontario, Canada, the is the major Canadian group to counsel and monitor outcomes of women using medications or NHPs, or of women exposed to chemicals, radiation or infection during pregnancy and lactation. OBJECTIVE: To create a network for research on NHPs during pregnancy and lactation by forming longstanding collaborations among Canadian medical and complementary and alternative medicine (CAM) practitioners and scientists. METHODOLOGY: MotherNature Network members participated in three 2-day workshops and three conference calls throughout the length of this study. Each member was responsible to lead discussions surrounding one theme and address the following: initiation; development; presentation; and synthesis of comments of all members on the designated theme. RESULTS: We prioritized areas in high need for future research and collaborative means to conduct such research. NHPs were prioritized for their importance for future study. Areas for the prospective collection of data on NHP use in pregnancy and lactation were identified. A research and business plan was developed for the long-term sustainability of the Network. CONCLUSIONS: The MotherNature Network is well-situated to create a new climate in Canada, where data are collected and interpreted on the effects and safety of NHPs during pregnancy and lactation.
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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 | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".