Primary healthcare system performance in low-income and middle-income countries: a scoping review of the evidence from 2010 to 2017
Bibliographic record
Abstract
INTRODUCTION: The 2018 Astana Declaration reaffirmed global commitment to primary healthcare (PHC) as a core strategy to achieve universal health coverage. To meet this potential, PHC in low-income and middle-income countries (LMIC) needs to be strengthened, but research is lacking and fragmented. We conducted a scoping review of the recent literature to assess the state of research on PHC in LMIC and understand where future research is most needed. METHODS: Guided by the Primary Healthcare Performance Initiative (PHCPI) conceptual framework, we conducted searches of the peer-reviewed literature on PHC in LMIC published between 2010 (the publication year of the last major review of PHC in LMIC) and 2017. We also conducted country-specific searches to understand performance trajectories in 14 high-performing countries identified in the previous review. Evidence highlights and gaps for each topic area of the PHCPI framework were extracted and summarised. RESULTS: We retrieved 5219 articles, 207 of which met final inclusion criteria. Many PHC system inputs such as payment and workforce are well-studied. A number of emerging service delivery innovations have early evidence of success but lack evidence for how to scale more broadly. Community-based PHC systems with supportive governmental policies and financing structures (public and private) consistently promote better outcomes and equity. Among the 14 highlighted countries, most maintained or improved progress in the scope of services, quality, access and financial coverage of PHC during the review time period. CONCLUSION: Our findings revealed a heterogeneous focus of recent literature, with ample evidence for effective PHC policies, payment and other system inputs. More variability was seen in key areas of service delivery, underscoring a need for greater emphasis on implementation science and intervention testing. Future evaluations are needed on PHC system capacities and orientation toward social accountability, innovation, management and population health in order to achieve the promise of PHC.
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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 | 0.023 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.031 | 0.036 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".