Knowledge transfer and translation: Examining how teratogen information is disseminated
Bibliographic record
Abstract
BACKGROUND: Well-executed knowledge transfer and translation (KT) has become a vital part of effective health management. Following the thalidomide disaster, women and their health care providers became fearful of medications and environmental exposures that could affect the health of the unborn child. Therefore, it is important to disseminate evidenced-based information to pregnant women and their health care providers, enabling them to make empowered decisions regarding exposures during pregnancy. OBJECTIVES: The objectives were twofold: (1) to explore the knowledge transfer process of teratology information from the research community to health care providers, pregnant women, and the general public; and (2) to examine how this impacts pregnant women and their health care providers who require this information. METHODS: We searched the peer reviewed literature (PUBMED, MEDLINE, and EMBASE), retrieved and examined original studies and review articles, and identified relevant data to evaluate how KT is conducted in this field. RESULTS: We found that KT and teratology information is very complex, with confusing information, over-estimated fears of teratogenicity, as well as unhelpful, often negatively biased information from the media. Of all the methods we identified, Teratogen Information Services (TIS) appears to conduct the most effective KT approaches in this field. CONCLUSION: It is evident that KT in this area needs improvement. Women and their health care providers are highly impacted by the type of teratology information they receive, affecting for example, deciding to terminate a wanted pregnancy or discontinue a needed pharmacotherapy. When disseminating information in this very sensitive and complex field, it is imperative that good KT strategies are used, encompassing the availability and appropriate interpretation of information. It is most important that an evidence-based decision is made to ensure the optimal outcome for both the mother and her unborn child.
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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 | 0.069 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".