Successful Community Midwives in Pakistan: An Asset-Based Approach
Bibliographic record
Abstract
In response to the low levels of skilled birth attendance in rural Pakistan, the government introduced a new cadre of community midwives (CMWs) in 2006. Assessments to-date have found that these CMWs have yet to emerge as significant providers for a number of sociocultural, geographic and financial reasons. However, a small number of CMWs have managed to establish functional practices in the private sector in conservative, infrastructure-challenged rural contexts. With an objective to highlight "what are the successful CMWs doing right given their context?" this paper adopts an asset-based approach to explore the experiences of the Pakistani CMWs who have managed to overcome the barriers and practice. We drew upon ethnographic data that was collected as part of a larger mixed methods study conducted in 2011-2012 in districts Jhelum and Layyah, Pakistan. Thirty eight CMWs, 45 other health care providers, 20 policymakers, 78 women, 35 husbands and 23 older women were interviewed. CMW clinics and practices were observed. Our data showed that only eight 8 out of 38 CMWs sampled were active providers. Poverty as a push factor to work and intrinsic individual-level characteristics that enabled the CMWs to respond successfully to the demands of the midwifery profession in the private sector emerged as the two key themes. Household poverty pushed the CMWs to work in this perceived low-status occupation. Their families supported them since they became the breadwinners. The successful CMWs also had an intrinsic sense of what was required to establish a private practice; they exhibited professionalism, had strong business sense and provided respectful maternity care. The study provides insight into how the program might improve its functioning by adapting its recruitment criteria to ensure selection of right candidates.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".