Barriers and facilitators to implementation of essential health benefits package within primary health care settings in low‐income and middle‐income countries: A systematic review
Bibliographic record
Abstract
BACKGROUND: One of the key requirements for achieving universal health coverage is the proper design and implementation of essential health benefits package (EHPs). We systematically reviewed the evidence on barriers and facilitators to the implementation of EHPs within primary health care settings in low-income and middle-income countries. METHODS: We searched multiple databases and the gray literature. Two reviewers completed independently and in duplicate data selection, data extraction, and quality assessment. We synthesized the findings according to the following health systems arrangement levels: governance, financial, and delivery arrangements. RESULTS: Ten studies met the eligibility criteria. At the governance level, key reported barriers were insufficient policymaker-implementer interactions, limited involvement of consumers and stakeholders, sub-optimal primary health care network arrangement, poor marketing and promotion of package, and insufficient coordination with community network. The key reported facilitator was the presence of a legal policy framework for package implementation. At the financial level, barriers included delays and inadequate remunerations to health care providers while facilitators included government and donor commitments to financing of package and flexibility in exploring new funding mechanisms. At the delivery level, barriers included inadequate supervision, poor facility infrastructure, limited availability of equipment and supplies, and shortages of workers. Facilitators included proper training and management of workforce, availability of female health workers, presence of clearly defined packages, and continuum of care, including referrals to promote comprehensive service delivery. CONCLUSION: We identified a set of barriers and facilitators that need to be addressed to ensure proper implementation of EHPs within primary health care settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".