What do we know about the needs and challenges of health systems? A scoping review of the international literature
Bibliographic record
Abstract
BACKGROUND: While there is an extensive literature on Health System (HS) strengthening and on the performance of specific HSs, there are few exhaustive syntheses of the challenges HSs are facing worldwide. This paper reports the findings of a scoping review aiming to classify the challenges of HSs investigated in the scientific literature. Specifically, it determines the kind of research conducted on HS challenges, where it was performed, in which health sectors and on which populations. It also identifies the types of challenge described the most and how they varied across countries. METHODS: We searched 8 databases to identify scientific papers published in English, French and Italian between January 2000 and April 2016 that addressed HS needs and challenges. The challenges reported in the articles were classified using van Olmen et al.'s dynamic HS framework. Countries were classified using the Human Development Index (HDI). Our analyses relied on descriptive statistics and qualitative content analysis. RESULTS: 292 articles were included in our scoping review. 33.6% of these articles were empirical studies and 60.1% were specific to countries falling within the very high HDI category, in particular the United States. The most frequently researched sectors were mental health (41%), infectious diseases (12%) and primary care (11%). The most frequently studied target populations included elderly people (23%), people living in remote or poor areas (21%), visible or ethnic minorities (15%), and children and adolescents (15%). The most frequently reported challenges related to human resources (22%), leadership and governance (21%) and health service delivery (24%). While health service delivery challenges were more often examined in countries within the very high HDI category, human resources challenges attracted more attention within the low HDI category. CONCLUSIONS: This scoping review provides a quantitative description of the available evidence on HS challenges and a qualitative exploration of the dynamic relationships that HS components entertain. While health services research is increasingly concerned about the way HSs can adopt innovations, little is known about the system-level challenges that innovations should address in the first place. Within this perspective, four key lessons are drawn as well as three knowledge gaps.
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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 | 0.017 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".