Kenya’s Health in All Policies strategy: a policy analysis using Kingdon’s multiple streams
Bibliographic record
Abstract
BACKGROUND: Health in All Policies (HiAP) is an intersectoral approach that facilitates decision-making among policy-makers to maximise positive health impacts of other public policies. Kenya, as a member of WHO, has committed to adopting HiAP, which has been included in the Kenya Health Policy for the period 2014-2030. This study aims to assess the extent to which this commitment is being translated into the process of governmental policy-making and supported by international development partners as well as non-state actors. METHODS: To examine HiAP in Kenya, a qualitative case study was performed, including a review of relevant policy documents. Furthermore, 40 key informants with diverse backgrounds (government, UN agencies, development agencies, civil society) were interviewed. Analysis was carried out using the main dimensions of Kingdon's Multiple Streams Approach (problems, policy, politics). RESULTS: Kenya is facing major health challenges that are influenced by various social determinants, but the implementation of intersectoral action focusing on health promotion is still arbitrary. On the policy level, little is known about HiAP in other government ministries. Many health-related collaborations exist under the concept of intersectoral collaboration, which is prominent in the country's development framework - Vision 2030 - but with no specific reference to HiAP. Under the political stream, the study highlights that political commitment from the highest office would facilitate mainstreaming the HiAP strategy, e.g. by setting up a department under the President's Office. The budgeting process and planning for the Sustainable Development Goals were found to be potential windows of opportunity. CONCLUSION: While HiAP is being adopted as policy in Kenya, it is still perceived by many stakeholders as the business of the health sector, rather than a policy for the whole government and beyond. Kenya's Vision 2030 should use HiAP to foster progress in all sectors with health promotion as an explicit goal.
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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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".