Matching safety to access: global actors and pharmacogovernance in Kenya- a case study
Bibliographic record
Abstract
BACKGROUND: The Kenyan government has sought to address inadequacies in its National Pharmaceutical Policy and the Pharmacy and Poisons Board's (PPB) medicines governance by engaging with global actors (e.g. the World Health Organization). Policy actors have influenced the way pharmacovigilance is defined, how challenges are understood and which norms are requisite to address drug safety issues. In this paper, we investigate the relationship between specific modes of engagement among global (exogenous) and domestic actors at the national and sub-national level to identify the positive or negative effect on pharmacovigilance and pharmacogovernance in Kenya. Pharmacogovernance is defined as the manner in which governing structures; policy instruments; institutional authority (e.g., ability to act, implement and enforce norms, policies and processes) and resources are managed to promote societal interests for patient safety and protection from adverse drug reactions (ADRs). Qualitative research methods that included key informant interviews and document analysis, were employed to investigate the relationship between global actors' patterns of engagement with national actors and pharmacogovernance in Kenya. RESULTS: Global actors' influence on pharmacogovernance and pharmacovigilance priorities in Kenya (e.g., legislation and adverse drug reaction surveillance) was positively perceived by key informants. We found that global actors' engagement with state actors produced positive and negative outcomes. Engagement with the PPB and Ministry of Health (MOH) that was characterized as dependent (advocacy, empowerment, delegated) or interdependent (collaborative, cooperative, consultative) was mostly associated with positive outcomes e.g., capacity building; strengthening legislation and stakeholder coordination. Fragmentation (independent engagement) hindered risk communication between public, private, and NGO health programs. CONCLUSION: A framework for assessing pharmacogovernance would support policy makers' evidence-based decision making regarding investments to strengthen capacity for pharmacovigilance and guide policies regarding the state and exogenous actor relationship pertaining to pharmacogovernance. Ideally, dependency on exogenous actors should be reduced while retaining consultative, collaborative, and cooperative engagement when inter-dependency is appropriate. The use of global actors to address Kenya's pharmacovigilance inadequacies leaves the country vulnerable to 1) ad hoc drug surveillance; 2) pharmacovigilance fragmentation; 3) shifting priorities; and 4) cross purpose interests.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".