Stakeholder participation in the COVID-19 pandemic preparedness and response plans: A synthesis of findings from 70 countries
Bibliographic record
Abstract
Stakeholder participation is a key component of a fair and equitable priority-setting in health. The COVID-19 pandemic highlighted the need for fair and equitable priority setting, and hence, stakeholder participation. To date, there is limited literature on stakeholder participation in the development of the pandemic plans (including the priority setting plans) that were rapidly developed during the pandemic. Drawing on a global study of national COVID-19 preparedness and response plans, we present a secondary analysis of COVID-19 national plans from 70 countries from the six WHO regions, focusing on stakeholder participation. We found that most plans were prepared by the Ministry of Health and acknowledged WHO guidance, however less than half mentioned that additional stakeholders were involved. Few plans described a strategy for stakeholder participation and/or accounted for public participation in the plan preparation. However, diverse stakeholders (including multiple governmental, non-governmental, and international organizations) were proposed to participate in the implementation of the plans. Overall, there was a lack of transparency about who participated in decision-making and limited evidence of meaningful participation of the community, including marginalized groups. The critical relevance of stakeholder participation in priority setting requires that governments develop strategies for meaningful participation of diverse stakeholders during pandemics such as COVID-19, and in routine healthcare priority setting.
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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 | 0.040 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".