Addressing Policy Coherence Between Health in All Policies Approach and the Sustainable Development Goals Implementation: Insights From Kenya
Bibliographic record
Abstract
BACKGROUND: Addressing health in the Sustainable Development Goals (SDGs) calls for intersectoral strategies that mutually enhance both health promotion and sustainable development. Health in All Policies (HiAP) approach aims to address this as well as promote ownership among key stakeholders. Kenya was at the forefront of adopting the SDGs and has committed to the HiAP approach in its Health Policy document for the period 2014-2030. This study aims to assess how the adoption of the HiAP approach can leverage on SDGs implementation in Kenya. METHODS: This is an exploratory case study using qualitative data and some descriptive quantitative data. The Organisation for Economic Co-operation and Development's (OECD's) eight building blocks for policy coherence on sustainable development was our guiding framework. Qualitative data was derived from a review of relevant peer-reviewed and grey literature, as well as 40 key informant interviews and analyzed in NVIVO. Quantitative data was accessed from the United Nations SDG indicator database and exported to Excel. RESULTS: Kenya has expressed a strong political commitment to achieving the SDGs and has now adopted HiAP. The study showed that Kenya can leverage on local level implementation and long-term planning horizons that it currently has in place to address the SDGs as it rolls out the HiAP approach. The SDGs could be mapped out against the sectors outlined in the Adelaide statement on HiAP. It is also possible to map out how various ministries could coordinate to effectively address HiAP and SDGs concurrently. Funding for HiAP was not addressed in the OECD framework. CONCLUSION: Kenya can advance a HiAP approach by leveraging the ongoing SDGs implementation. This will be made possible by facilitating coordinated intersectoral action both at national and local level. Funding for HiAP is crucial for its propagation, especially in low- and middle-income countries (LMICs) and can be considered in the budgetary allocations for SDGs.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".