Building trust and enhancing localization to improve access to women’s health services in Afghanistan
Bibliographic record
Abstract
Abstract Background Over 34 million people in Afghanistan have suffered from death and devastation for the last four decades as a result of conflict. Women and children have borne the brunt of this devastation. Afghanistan has some of the poorest health indicators in the world for women and children. In the midst of armed conflict, providing essential healthcare in remote regions in the throws of conflict remains a challenge, which is being addressed the Mobile Health Teams through Afghan Red Crescent (ARCS). To overcome socio-cultural barriers, ARCS MHTs have used local knowledge to hire female staff as part of the MHTs along with their male relatives as part of MHT staff. The present study was conducted to explore the impact of engaging female health workers as part of MHTs in conflict zones within Afghanistan on access, availability and utilization of maternal and child health care. Methods Quantitative descriptive and time-trend analysis were used to evaluate impact of introduction of female health workers. Qualitative data is being analyzed to assess the possibilities and implications of engaging female health workers in the delivery of health services. Results Preliminary results show a 96% increase in uptake of services for expectant mothers over the last four years. Average of 18 thousand services provided each month by MHTs, 70% for women and children. Service delivery for women and children significantly increased over time (p < 0.05) after inclusion of female health workers in MHTs. Delivery of maternity care services showed a more significant increase (p < 0.001). Time trend and qualitative analyses is ongoing. Conclusions Introduction of female health workers significantly improved uptake of health care services for women and children especially in extremely isolated areas controlled by armed groups in Afghanistan. Engaging with local stakeholders is essential for delivery of health services for vulnerable populations in fragile settings like Afghanistan. Key messages Understanding cultural norms results in socially acceptable solutions to barriers in delivery of healthcare services and leads to improvements in access for women and children in fragile settings. Building local partnerships and capacities and using local resources result in safe, efficient and sustainable delivery of healthcare services for vulnerable populations in fragile settings.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".