How Women Evaluate Birth Challenges: Analysis of Web-Based Birth Stories
Bibliographic record
Abstract
BACKGROUND: Birth stories provide an intimate glimpse into women's birth experiences in their own words. Understanding the emotions elicited in women by certain types of behaviors during labor and delivery could help those in the health care community provide better emotional care for women in labor. OBJECTIVE: The aim of this study was to understand which supportive reactions and behaviors contributed to negative or positive emotions among women with regard to their labor and delivery experience. METHODS: We sampled 10 women's stories from a popular blog that described births that strayed from the plan. Overall, 90 challenging events that occurred during labor and delivery were identified. Each challenge had an emotionally positive, negative, or neutral evaluation by the woman. We classified supportive and unsupportive behaviors in response to these challenges and examined their association with the woman's emotional appraisal of the challenges. RESULTS: Overall, 4 types of behaviors were identified: informational inclusion, decisional inclusion (mostly by health care providers), practical support, and emotional support (mostly by partners). Supportive reactions were not associated with emotional appraisal; however, unsupportive reactions were associated with women appraising the challenge negatively (Fisher exact test, P=.02). CONCLUSIONS: Although supportive behaviors did not elicit any particular emotion, unsupportive behaviors did cause women to view challenges negatively. It is worthwhile conducting a larger scale investigation to observe what happens when patients express their needs, particularly when challenges present themselves during labor, and health care professionals strive to cater to them.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".