Quality of care in the context of universal health coverage: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Universal health coverage (UHC) is an emerging priority of health systems worldwide and central to Sustainable Development Goal 3 (target 3.8). Critical to the achievement of UHC, is quality of care. However, current evidence suggests that quality of care is suboptimal, particularly in low- and middle-income countries. The primary objective of this scoping review was to summarize the existing conceptual and empirical literature on quality of care within the context of UHC and identify knowledge gaps. METHODS: We conducted a scoping review using the Arksey and O'Malley framework and further elaborated by Levac et al. and applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews reporting guidelines. We systematically searched MEDLINE, EMBASE, CINAHL-Plus, PAIS Index, ProQuest and PsycINFO for reviews published between 1 January 1995 and 27 September 2021. Reviews were eligible for inclusion if the article had a central focus on UHC and discussed quality of care. We did not apply any country-based restrictions. All screening, data extraction and analyses were completed by two reviewers. RESULTS: Of the 4128 database results, we included 45 studies that met the eligibility criteria, spanning multiple geographic regions. We synthesized and analysed our findings according to Kruk et al.'s conceptual framework for high-quality systems, including foundations, processes of care and quality impacts. Discussions of governance in relation to quality of care were discussed in a high number of studies. Studies that explored the efficiency of health systems and services were also highly represented in the included reviews. In contrast, we found that limited information was reported on health outcomes in relation to quality of care within the context of UHC. In addition, there was a global lack of evidence on measures of quality of care related to UHC, particularly country-specific measures and measures related to equity. CONCLUSION: There is growing evidence on the relationship between quality of care and UHC, especially related to the governance and efficiency of healthcare services and systems. However, several knowledge gaps remain, particularly related to monitoring and evaluation, including of equity. Further research, evaluation and monitoring frameworks are required to strengthen the existing evidence base to improve UHC.
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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".