The planning of a national breastfeeding educational intervention for medical residents
Bibliographic record
Abstract
BACKGROUND: Breastfeeding is the ideal form of nutrition for newborns, yet our recent pan-Canadian study showed that the knowledge, attitudes, and beliefs of primary care pediatricians and family physicians are suboptimal with regard to breastfeeding. OBJECTIVE: We aim to develop, implement, and evaluate a national breastfeeding educational intervention at the postgraduate residency level. METHODS: Our initial development process is informed by Kern's approach to curriculum development. To date, we have completed breastfeeding education needs assessment surveys of both practicing physicians and medical residents. We have also developed learning outcomes as well as possible strategies for implementing and evaluating this future educational intervention. RESULTS: The results of our needs assessment surveys provided a rationale to develop a breastfeeding educational intervention for medical residents. Through stakeholder consultations, we have developed five initial learning outcomes for a national breastfeeding educational intervention. We have also identified promising strategies for implementing and evaluating the intervention. CONCLUSIONS: This systematic process has provided an opportunity to create a national breastfeeding educational intervention for medical residents. It has fostered collaboration between experts and knowledge users, with the goal of impacting breastfeeding rates and duration of women, which will lead to improved maternal and child outcomes.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".