The Effects of Introducing Prenatal Breastfeeding Education in the Obstetricians' Waiting Rooms
Bibliographic record
Abstract
The literature has reported great benefits of breastfeeding for both the mother and newborn. In light of the low numbers of women breastfeeding in Ontario, even fewer women attending prenatal classes, and limited amount of cost free prenatal classes available, a need was identified to consider alternate modes of prenatal breastfeeding education. The purpose of this thesis was to explore the effects of providing self-directed study materials in the forms of breastfeeding education videos, smartphone applications, and reading materials to prenatal women during their third trimester appointments in the obstetrician’s waiting room. The idea of presenting innovative modes of prenatal breastfeeding education in the obstetrician’s waiting room was brought about to introduce the breastfeeding topic to women who were unfamiliar with breastfeeding, enhance the learning experiences women may have already had in prenatal classes, and help to stimulate breastfeeding conversations with their nurses and obstetricians. This study found the support of a significant other was significantly related to breastfeeding intention. Also, introducing the prenatal education resources in the obstetrician’s waiting room, significantly increased breastfeeding attitudes and knowledge among the participant group. Introducing prenatal breastfeeding education in the obstetrician’s waiting room can potentially increase breastfeeding rates within Windsor-Essex County, which can in turn, improve the condition of maternal and newborn health within the community.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".