Medicalization and Fear: A Midwifery View of the Phenomenon and the Backlash
Bibliographic record
Abstract
The phenomenon of medicalization in the United States is something that midwives must deal with on a daily basis, and it has far-reaching consequences for women’s health. This article examines the culture of birth in the U.S. and how medicalization has manifested itself as a social norm from the perspectives of working certified nurse midwives in hospitals and birth centers. It explores the philosophy of the medicalized birth, the impact of technology on the perpetuation of medicalization in United States’ culture, and the fear of this phenomenon that midwives are starting to see in practice, which adversely affects their work. This article argues that advances in and dependence on obstetrical technology have enabled medicalization to continue and created a response of fear from women who worry this phenomenon will negatively affect their birthing experience. My research demonstrates that midwives recognize that the dominance of technology in health care has shaped not only how birth has become medicalized, but also how women are responding to this “technocratic birth” and how navigating women’s fears about hyper-medicalization has become a central part of midwives’ practice. Through Michel Foucault’s theory of biopower and Robbie Davis-Floyd’s idea of the “technocratic birth,” this article explains how medicalization depends on technology and why midwives are seeing an adverse reaction from women who fear these trends.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.088 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".