How and why buy-in for health in all policies was facilitated in Ecuador: a realist case study of Plan Nacional para el Buen Vivir
Bibliographic record
Abstract
BACKGROUND: In 2008, Ecuador introduced Plan Nacional para el Buen Vivir (PNBV; National Plan for Good Living), which was widely recognized as a promising example of Health in All Policies (HiAP) due to the integration of policy sectors on health and health equity objectives. PBNV was implemented through three successive plans (2009-2013, 2013-2017, 2017-2021). In a time of widening global health inequities, there is growing interest in understanding how politics and governance shape HiAP implementation. The objective of this study was to test specific hypotheses about how, why, to what extent, and under what circumstances HiAP was implemented in Ecuador. METHODS: An explanatory case study approach (HiAP Analysis using Realist Methods on International Case Studies-HARMONICS) was used to understand the processes that hindered or facilitated HiAP implementation. Realist methods and systems theory were employed to test hypotheses through analysis of empirical and grey literature, and 19 key informant interviews. This case study focused on processes related to buy-in for a HiAP approach by diverse policy sectors, particularly in relation to the strong mandate and transformative governance approach that were introduced by then-President Rafael Correa's administration to support PNBV. RESULTS: The mandate and governance approach of the HiAP approach achieved buy-in for implementation across diverse sectors. Support for the hypotheses was found through direct evidence about buy-in for HiAP implementation by policy sectors; and indirect evidence about allocation of governmental resources for HiAP implementation. Key mechanisms identified included: influence of political elites; challenges in dealing with political opposition and 'siloed' ways of thinking; and the role of strategies and resources in motivating buy-in. CONCLUSION: In Ecuador, political elites were a catalyst for mechanisms that impacted buy-in and government funding for HiAP implementation. They raised awareness among policy sectors initially opposed to PNBV about the rationale for changing governance practices, and they provided financial resources to support efforts related to PNBV. Specific mechanisms help explain these phenomena further. Future studies should examine ways that PNBV may have been an impediment to health equity for some marginalized groups while strengthening HiAP implementation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".