Shared health governance, mutual collective accountability, and transparency in COVAX: A qualitative study triangulating data from document sampling and key informant interviews
Bibliographic record
Abstract
Background: To facilitate global COVID-19 vaccine equity, the World Health Organization, the Coalition for Epidemic Preparedness Innovations, the Global Alliance for Vaccines and Immunizations, and the United Nations Children's Fund supported the COVID-19 Vaccine Global Access (COVAX) partnership. COVAX's goals may have best been pursued through shared health governance - a theory of global health governance based on six premises, in which global health actors collaborate to achieve a shared goal. Shared health governance employs a framework for accountability termed "mutual collective accountability", in which actors hold each other accountable for achieving their goal, thus relying on transparency with one another. Methods: We conducted a multi-method qualitative study triangulating document analysis and key informant interviews to address the question: To what extent did COVAX employ shared health governance, mutual collective accountability, and transparency? We thus aimed to explore the governance structures and accountability and transparency mechanisms in COVAX and determine whether these constituted shared health governance and mutual collective accountability. Results: We identified 117 documents and interviewed 20 key informants. Our findings suggest that COVAX's co-convening organisations were governed by their individual formal governance mechanisms, while each was formally accountable to its own leadership team, resulting in challenges when activities and decisions involved collaboration between organisations. Furthermore, COVAX's governance lacked transparency, as there was little public information about their decision-making processes and operations, including information about the algorithm with which they make vaccine allocation decision, possibly contributing to its inability to achieve its goals. Conclusions: The COVAX partnership only achieved four of the six premises of shared health governance. Since actors involved in COVAX did not hold one another accountable for their role in the partnership, it did not employ mutual collective accountability, while also lacking in transparency. Although these results do not entirely explain COVAX's shortcomings, they contribute to evidence about the roles of good governance, transparency, and accountability in large global health initiatives and underscore failures of the current global governance system.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".