A framework for explaining the role of values in health policy decision-making in Latin America: a critical interpretive synthesis
Bibliographic record
Abstract
BACKGROUND: Although values underpin the goals pursued in health systems, including how health systems benefit the population, it is often not clear how values are incorporated into policy decision-making about health systems. The challenge is to encompass social/citizen values, health system goals, and financial realities and to incorporate them into the policy-making process. This is a challenge for all health systems and of particular importance for Latin American (LA) countries. Our objective was to understand how and under what conditions societal values inform decisions about health system financing in LA countries. METHODS: A critical interpretive synthesis approach was utilised for this work. We searched 17 databases in December 2016 to identify articles written in English, Spanish or Portuguese that focus on values that inform the policy process for health system financing in LA countries at the macro and meso levels. Two reviewers independently screened records and assessed them for inclusion. One researcher conceptually mapped the included articles, created structured summaries of key findings from each, and selected a purposive sample of articles to thematically synthesise the results across the domains of agenda-setting/prioritisation, policy development and implementation. RESULTS: We identified 5925 references, included 199 papers, and synthesised 68 papers. We identified 116 values and developed a framework to explain how values have been used to inform policy decisions about financing in LA countries. This framework has four categories - (1) goal-related values (i.e. guiding principles of the health system); (2) technical values (those incorporated into the instruments adopted by policy-makers to ensure a sustainable and efficient health system); (3) governance values (those applied in the policy process to ensure a transparent and accountable process of decision-making); and (4) situational values (a broad category of values that represent competing strategies to make decisions in the health systems, their influence varying according to the four factors). CONCLUSIONS: It is an effort to consolidate and explain how different social values are considered and how they support policy decision-making about health system financing. This can help policy-makers to explicitly incorporate values into the policy process and understand how values are supporting the achievement of policy goals in health system financing. TRIAL REGISTRATION: The protocol was registered with PROSPERO, ID=CRD42017057049 .
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".