Unintended Consequences of “Breast Is Best” Messaging on Mothers: An Autoethnography
Bibliographic record
Abstract
Purpose: To describe the breastfeeding experiences of a dietitian and mother so as to expose dominant discourses reinforcing expert-driven imperatives to breastfeed. Methods: Professional experiences and personal challenges related to breastfeeding promotion are described, analyzed, and interpreted using autoethnography. The social ecological model (SEM) is used as a sensitizing concept to guide the organization, presentation, and analysis of experiences. Results: Data were organized into two discussion themes: breastfeeding promotion practices and “failure” to breastfeed. Dominant discourses reinforcing expert-driven imperatives to breastfeed are revealed, including health as a duty, intensive motherhood, and mother blame. Discourses promoting or reinforcing breastfeeding simultaneously judge and denormalize formula-feeding. Conclusions: Contemporary breastfeeding promotion messages and strategies are quiet coercions used to influence infant-feeding decisions and do not support the principles of evidence-based practice, person-centred care, and informed choice.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".