The maternal drinking history guide: development of a national educational tool.
Bibliographic record
Abstract
BACKGROUND: The National Taskforce for the development of screening tools for FASD has identified maternal drinking as a critical area that should be screened. We describe the steps of development and implementation of a knowledge translation program for health care providers. The slide presentation is attached in English and French to allow its maximal use. METHODS: In 2010, the National Taskforce for the development of screening tools for FASD identified maternal drinking as a critical area that should be screened. The systematic review and associated recommendations have been published and were included in the toolkit developed by the Canadian Association of Paediatric Health Centres with funding support from the Public Health Agency of Canada. Effective inquiry of maternal drinking can be conducted at three levels: Primary level, as part of practice-based screening; Level 2 use of structured questionnaires; and Level 3 laboratory-based screening. CONCLUSION: It was acknowledged that most physicians do not ask women of reproductive age questions regarding their drinking habits, and the Taskforce was seriously concerned that even an effective guide may not change practice at the primary level. To that end, the Taskforce developed a three phase Knowledge Translation plan, to ensure that the educational program developed will be optimally effective for Canadian healthcare providers.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".