Enhancing Health Professionals’ Competencies to Support Breastfeeding Mothers in Quebec, Canada: A Case by Isabelle Michaud-Létourneau, Jacqueline Wassef, Julie Lauzière, Laura Rosa Pascual, Marion Gayard, and Micheline Beaudry
Bibliographic record
Abstract
This chapter is written by Dr. Isabelle Michaud-Létourneau and colleagues. It examines the opportunities to strengthen human dignity for mothers and their babies through the lens of breastfeeding and the health professionals who help support breastfeeding practices. The chapter uses the functions of the social process, decision process, and problem orientation to (1) assess the extent of the lack of breastfeeding competencies of health professionals, (2) document an initiative designed to address this challenge, and (3) carry out actions to foster organizational policy changes. The first section below outlines the problem and case goals. The second section describes the authors’ standpoints and methods used. The following three sections outline the social process, decision process, and problem orientation, respectively. The last section ends with recommendations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".