Implementation of health and health-related sustainable development goals: progress, challenges and opportunities – a systematic literature review
Bibliographic record
Abstract
INTRODUCTION: While health is one of the Sustainable Development Goals (SDGs), many other 'health-related' goals comprise determinants of health. Integrated implementation across SDGs is needed for the achievement of Agenda 2030. While existing literature is rich in normative recommendations about potentially useful approaches, evidence of implementation strategies being adopted by countries is limited. METHODS: We conducted a systematic review with qualitative synthesis of findings using peer reviewed and grey literature from key databases. We included publications examining implementation of health and health-related SDGs (HHSDGs) at national or subnational level published between June 2013 and July 2019. RESULTS: Of the 32 included publications, 24 provided information at the national level while eight provided information for multiple countries or regions. Our findings indicate that high-level political commitment is evident in most countries and HHSDGs are being aligned with existing national development strategies and plans. A multisectoral, integrated approach is being adopted in institutional setups but evidence on effectiveness of these approaches is limited. Funding constraints are a major challenge for many countries. HHSDGs are generally being financed from within existing funded plans and, in some instances, through SDG-specific budgeting and tracking; additional funding is being mobilised by increasing domestic taxation and subsidisation, and by collaborating with development partners and private sector. Equity is being promoted by improving health service access through universal health coverage and social insurance schemes, especially for disadvantaged populations. Governments are collaborating with development partners and UN agencies for support in planning, institutional development and capacity building. However, evidence on equity promotion, capacity building initiatives and implementation approaches at subnational level is limited. Lack of coordination among various levels of government emerges as a key challenge. CONCLUSION: strengthening implementation of multisectoral work, capacity building, financial sustainability and data availability are key considerations to accelerate implementation of HHSDGs.
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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 | 0.038 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".