Promoting awareness of fetal alcohol spectrum disorder among health professionals and the public through nursing faculty champions
Bibliographic record
Abstract
Fetal alcohol spectrum disorder (FASD) affects all communities and is an underestimated problem worldwide and in China. FASD is the most common preventable cause of intellectual disabilities and behavior problems. However, prevention efforts require knowledge about FASD, importantly, the education of health professionals who communicate that knowledge to the public during care administered in diverse practice settings. Implementing a nursing faculty champions (charismatic advocates for FASD prevention belief, practice, program, policy and/or technology) model to advocate for educating Chinese nurses, nursing students, other health professionals, and the public about FASD is a logical, quality-driven, healthcare action. The actions undertaken by one nurse champion, a Capital Medical University Chinese professor, to promote FASD awareness among Chinese health professionals and the public population will be presented. Through this faculty nurse champion, thousands of Chinese health providers and public citizens were educated regarding FASD. Planned next steps include enrolling more Chinese nursing faculty champions, developing nursing curricula at Capital Medical University, and increasing research attention on FASD. Nurse faculty champions are an effective and practical method to promote FASD awareness among Chinese health professionals and the public.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".