Factors influencing performance by contracted non-state providers implementing a basic package of health services in Afghanistan
Bibliographic record
Abstract
BACKGROUND: In 2002 Afghanistan's Ministry of Public Health (MoPH) and its development partners initiated a new paradigm for the health sector by electing to Contract-Out (CO) the Basic Package of Health Services (BPHS) to non-state providers (NSPs). This model is generally regarded as successful, but literature is scarce that examines the motivations underlying implementation and factors influencing program success. This paper uses relevant theories and qualitative data to describe how and why contracting out delivery of primary health care services to NSPs has been effective. The main aim of this study was to assess the contextual, institutional, and contractual factors that influenced the performance of NSPs delivering the BPHS in Afghanistan. METHODS: The qualitative study design involved individual in-depth interviews and focus group discussions conducted in six provinces of Afghanistan, as well as a desk review. The framework for assessing key factors of the contracting mechanism proposed by Liu et al. was utilized in the design, data collection and data analysis. RESULTS: While some contextual factors facilitated the CO (e.g. MoPH leadership, NSP innovation and community participation), harsh geography, political interference and insecurity in some provinces had negative effects. Contractual factors, such as effective input and output management, guided health service delivery. Institutional factors were important; management capacity of contracted NSPs affects their ability to deliver outcomes. Effective human resources and pharmaceutical management were notable elements that contributed to the successful delivery of the BPHS. The contextual, contractual and institutional factors interacted with each other. CONCLUSION: Three sets of factors influenced the implementation of the BPHS: contextual, contractual and institutional. The MoPH should consider all of these factors when contracting out the BPHS and other functions to NSPs. Other fragile states and countries emerging from a period of conflict could learn from Afghanistan's example in contracting out primary health care services, keeping in mind that generic or universal contracting policies might not work in all geographical areas within a country or between countries.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".