Women's Emotional Responses to their Unplanned Caesarean Deliveries: In Women's Words
Bibliographic record
Abstract
In Canada, one in five women can now expect to deliver their baby by caesarean section. For some women, this method of delivery creates little concern, but for others, birth by caesarean causes emotional trauma that can last for years. Childbirth was historically regarded as a natural event and was undertaken with little assistance from health professionals. However, with urbanization and medical advancements, childbirth soon became a medically managed process. During the Women's Health Movement of the 1970's, women reacted to the medicalization of birth by calling attention to the emotional reactions of women following childbirth, with particular attention paid to deliveries by caesarean section. This paper discusses interviews with five women who sought the assistance of a community support and awareness group following a negative emotional response to an unplanned caesarean section. Qualitative research methods were used in order to capture the participants' unique experiences during and after childbirth. The women described feelings of fear, failure, disappointment, and loss of control. They perceived that the medical staff was generally uncaring and dismissive of their concerns. Each participant felt that the support group was instrumental in helping them to recover from the trauma of their birth experience, but also reported that they would have appreciated the opportunity to speak with a social worker following the birth.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".