Elective caesarean section as a transformative technological process: players, power and context
Bibliographic record
Abstract
AIM: In this paper we present a critical analysis of the debate surrounding elective caesarean section using the Social Construction of Technology perspective as a framework of analysis. BACKGROUND: The rate of caesarean section births is increasing worldwide in industrialized countries. Reasons given for the increase include women's characteristics, care providers' attitudes, prevention of pelvic floor disorders and adverse outcomes. DATA SOURCES: CINAHL, PubMed, Ovid, Academic Search Premier and Cochrane Data bases were searched for the years 2000 to 2007 using search terms elective caesarean section, caesarean section on demand and maternal choice caesarean section. DISCUSSION: The social constructivist approach explains how caesarean section as a mode of delivery has been transformed from an emergency to an elective procedure. Analysing elective caesarean section as a socially constructed technological process exposes positions taken by obstetricians, midwives, perinatal nurses and women, including the power dynamics and contextual influences. CONCLUSION: The Social Construction of Technology perspective creates space for perinatal nurses to examine the implications of the use and meaning of elective caesarean section in a broader social context. Examining elective caesarean section from the Social Construction of Technology perspective exposes an emphasis on safety and risk for the foetus, while avoiding the equally important goal of promoting optimal postnatal health for mothers and infants. The Social Construction of Technology perspective highlights how those who define the problem control the solution.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".