Impact of conflict on maternal and child health service delivery – how and how not: a country case study of conflict affected areas of Pakistan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: In conflict affected countries, healthcare delivery remains a huge concern. Pakistan is one country engulfed with conflict spanning various areas and time spans. We aimed to explore the effect of conflict on provision of reproductive, maternal, newborn, child and adolescent health and nutrition (RMNCAH&N) services and describe the contextual factors influencing the prioritization and implementation in conflict affected areas of Pakistan (Balochistan and FATA). METHOD: We conducted a secondary quantitative and a primary qualitative analysis. For the quantitative analysis, we stratified the various districts/agencies of Balochistan and FATA into the conflict categories of minimal-, moderate- and severe based on accessibility to health services through a Delphi methodology with local stakeholders and implementing agencies and also based on battle-related deaths (BRD) information from Uppsala Conflict Data Program (UCDP). The coverage of RMNCAH&N indicators across the continuum of care were extracted from the demographic and health surveys (DHS) and district health information system (DHIS). We conducted a stratified descriptive analysis and multivariate analysis using STATA version 15. The qualitative data was captured by conducting key informant interviews of stakeholders working in government, NGOs, UN agencies and academia. All the interviews were audiotaped which were transcribed, translated, coded and analyzed on Nvivo software version 10. RESULTS: The comparison of the various districts based on the severity of conflict through Delphi process showed that the mean coverage of various RMNCAH&N indicators in Balochistan were significantly lower in severe- conflict districts when compared to minimal conflict districts, while there was no significant difference between moderate and severe conflict areas. There was no reliable quantitative data available for FATA. Key factors identified through qualitative analysis, which affected the prioritization and delivery of services included planning at the central level, lack of coordination amongst various hierarchies of the government and various stakeholders. Other factors included unavailability of health workforce especially female workers, poor quality of healthcare services, poor data keeping and monitoring, lack of funds and inconsistent supplies. Women and child health is set at a high priority but capacity gap at service delivery, resilience from health workers, insecurity and poor infrastructure severely hampers the delivery of quality healthcare services. CONCLUSION: Conflict has severely hampered the delivery of health services and a wholesome effort is desired involving coordination amongst various stakeholders. The multiple barriers in conflict contexts cannot be fully mitigated, but efforts should be made to negate these as much as possible with good governance, planning, efficiency and transparency in utilization of available resources.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it