Leveraging public engagement to improve healthcare quality: The role of community and stakeholder engagement in Colombia’s National Quality of Care Strategy
Bibliographic record
Abstract
Public engagement, also referred to as community and stakeholder engagement (CSE), and high-quality care are core components of strategies to achieve universal health coverage (UHC), one of the United Nations Sustainable Development Goals. As part of the movement toward achieving UHC, Colombia has developed one of the first national quality of care strategies in the Latin America and Caribbean region. However, the degree to which public engagement was considered in the development and implementation of Colombia's National Quality of Care Strategy (the Strategy) is not clearly understood. With a growing global consensus on the importance of public engagement in health systems and policy, we use a qualitative case study comprising a document analysis followed by qualitative interviews, to explore how CSE has been considered in the design and implementation of the Strategy. In an analysis guided by the Lavery framework for CSE, we address the following three research objectives: 1) describe how community and stakeholder engagement is reflected in the Strategy; 2) explore approaches undertaken to engage community and stakeholders in the development and implementation of the Strategy and their perceived effectiveness; and 3) report on strengths and opportunities for improving CSE in health policymaking in Colombia. Our findings demonstrate a strong written commitment to CSE. However, the implementation of engagement strategies fell short in including community (i.e., patients and citizens), particularly those from structurally marginalized communities, due in part to inconsistent political and financial support and the absence of evaluation mechanisms. These findings have important implications for Colombia and other comparable jurisdictions aiming to enhance public engagement in health policy making. Our study highlights the need to move beyond symbolic participation toward inclusive, well-resourced, and systematically evaluated engagement strategies.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".