Impact of service provision platforms on maternal and newborn health in conflict areas and their acceptability in Pakistan: a systematic review
Bibliographic record
Abstract
Various models and strategies have been implemented over the years in different parts of the world to improve maternal and newborn health (MNH) in conflict affected areas. These strategies are based on specific needs and acceptability of local communities. This paper has undertaken a systematic review of global and local (Pakistan) information from conflict areas on platforms of health service provision in the last 10 years and information on acceptability from local stakeholders on effective models of service delivery; and drafted key recommendations for improving coverage of health services in conflict affected areas. The literature search revealed ten studies that described MNH service delivery platforms. The results from the systematic review showed that with utilisation of community outreach services, the greatest impacts were observed in skilled birth attendance and antenatal consultation rates. Facility level services, on the other hand, showed that labour room services for an internally displaced population (IDP) improved antenatal care coverage, contraceptive prevalence rate and maternal mortality. Consultative meetings and discussions conducted in Quetta and Peshawar (capitals of conflict affected provinces) with relevant stakeholders revealed that no systematic models of MNH service delivery, especially tailored for conflict areas, are available. During conflict, even previously available services and infrastructure suffered due to various barriers specific to times of conflict and unrest. A number of barriers that hinder MNH services were discussed. Suggestions for improving MNH services in conflict areas were also laid down by participants. The review identified some important steps that can be undertaken to mitigate the effects of conflict on MNH services, which include: improve provision and access to infrastructure and equipment; development and training of healthcare providers; and advocacy at different levels for free access to healthcare services and for the introduction of the programme model in existing healthcare system. The obligation is enormous, however, for a sustainable programme, it is important to work closely with both the IDP and host community, and collaborating with the government and non-government organisations.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".