Challenges to achieving appropriate and equitable access to Caesarean section: ethnographic insights from rural Pakistan
Bibliographic record
Abstract
Access to Caesarean section (C-section) remains inadequate for some groups of women while others have worryingly high rates. Understanding differential receipt demands exploration of the socio-cultural, and political economic, characteristics of the health systems that produce them. This extensive institutional ethnography investigated under- and over-receipt of C-section in two rural districts in Pakistan - Jhelum and Layyah. Data were collected between November and July 2013 using semi-structured interviews from a randomly selected sample of 11 physicians, 38 community midwives, 18 Lady Health Visitors and nurses and 15 Traditional Birth Attendants. In addition, 78 mothers, 35 husbands and 23 older women were interviewed. The understandings of birth by C-section held by women and their family members were heavily shaped by gendered constructions of womanhood, patient-provider power differentials and financial constraints. They considered C-section an expensive and risky procedure, which often lacked medical justification, and was instead driven by profit motive. Physicians saw C-section as symbolizing obstetric skill and status and a source of legitimate income. Physician views and practices were also shaped by the wider health care system characterized by private practice, competition between providers and a lack of regulation and supervision. These multi-layered factors have resulted in both unnecessary intervention, and missed opportunities for appropriate C-sections. The data indicate a need for synergistic action at patient, provider and system levels. Recommendations include: improving physician communication with patients and family so that the need for C-section is better understood as a life-saving procedure, challenging negative attitudes and promoting informed decision-making by mothers and their families, holding physicians accountable for their practice and introducing price caps and regulations to limit financial incentives associated with C-sections. The current push for privatization of health care in low-income countries also needs scrutiny given its potential to encourage unnecessary intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".