How to analyze multistakeholder collaboration for infectious disease outbreaks: a scoping review
Bibliographic record
Abstract
Abstract Introduction Effective preparedness and response to infectious disease (ID) outbreaks demand collaboration among diverse stakeholders from public health and beyond. However, the organization and roles of stakeholders vary widely across countries and have evolved notably during the COVID-19 pandemic. Novel methodologies are essential to analyze and bolster collaboration in this context. We conducted a scoping review to explore methods utilized for evaluating multistakeholder collaboration in ID outbreak preparedness and response. Methods We comprehensively searched scientific and grey literature using keywords such as evaluation of collaboration, stakeholder analysis, (multi)stakeholder/multisectoral collaboration, and network coordination in PubMed, GoogleScholar and Google. Given the limited literature on ID outbreaks, we expanded our scope to include other public health emergencies (PHE) and related fields. Publications were analyzed to discern their aims, fields served, and methodologies employed. Results Twenty pertinent publications from Africa, Europe, North America, and Oceania were identified. These publications addressed diverse aims and fields, including stakeholder identification, network mapping in outbreak management and One Health initiatives, enhancing multistakeholder/ multisectoral collaboration for PHE response, primary care partnerships, and evaluating governance of networks in public policy. Methodologies encompassed network analysis, stakeholder mapping, and evaluative frameworks tailored to specific contexts. Common themes included trust-building, shared goals, decision-making processes, and information exchange. Conclusions The literature presents varied dimensions crucial for developing methodologies to evaluate multistakeholder collaboration in ID outbreak contexts. These evaluations ultimately will lead to strengthened multisectoral collaboration and coordination, and better preparedness and response for ID outbreaks. Key messages • Effective evaluation methodologies are crucial for enhancing multistakeholder collaboration in infectious disease outbreak preparedness and response worldwide. • Diverse approaches from various sectors offer valuable insights for developing tailored evaluation methods to assess collaboration in specific public health contexts.
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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 | 0.083 | 0.284 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.049 | 0.036 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".