Increasing awareness of the connection between breastfeeding and mental health through an educational session for healthcare providers
Bibliographic record
Abstract
Aim of the study The relationship between breastfeeding and mental health is complex and complicated by various confounding factors. Although no direct causative link has been established, several trends have arisen in the literature. The aim of this study was to see if an educational session on these trends could increase awareness of how breastfeeding affects mental health in women. Specifically, we aimed to see if this session could increase awareness in a group of clinicians (nurses, physicians, and lactation consultants) who provide care in the peripartum. Subject or material and methods A 45-minute education session was offered to health care providers for members of this population. The goal of this session was to increase awareness of how breastfeeding impacts mental health and how, when it is going well, it can positively affect mood. The session also emphasized the importance of support in the perinatal period. Results The session was evaluated for changes in awareness and knowledge and perceived usefulness and relevance via a short pre-and post- 8 question survey. There were significant differences in the post answers for Questions 1-5 compared to the pre- answers. Discussion These particular questions dealt with awareness and perceived relevance of the topic, the understanding of the complexity of the topic, perceived knowledge of the topic, and training on the topic. Conclusions These findings suggest that an educational session may improve awareness, knowledge levels, and perceived importance of this topic.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".