Exploring the dimensions of success in health crisis management: An exploratory qualitative study in Guinea
Bibliographic record
Abstract
The management of health crises entails a comprehensive spectrum of actions, spanning from prevention and preparedness planning, through resource allocation, communication strategies, stakeholder coordination, community involvement, intervention implementation, and culminating in recovery and rehabilitation phases, with additional complexities in developing countries. This article aims to explore the concept of success in health crisis management by conducting a literature review and analyzing qualitative data collected in Guinea in 2022. The study seeks to identify key dimensions that contribute to the comprehensive evaluation of success in health crisis response and recovery. The research design employed for this study is a qualitative approach, specifically utilizing semi-structured interviews with international, national, and local stakeholders involved in health crisis management in Guinea. Effective crisis management is crucial for public health but lacks comprehensive research identifying key success factors. This gap limits evidence-based guidelines for crisis preparedness, response, and recovery. While some literature covers aspects like timely actions and resource mobilization, detailed studies on success conditions are rare. More empirical research is needed to guide policymakers and healthcare professionals in developing robust health crisis management frameworks. The study underscores the multifaceted nature of successful health crisis management, emphasizing elements like timeliness, resource mobilization, transparent communication, socio-economic impacts mitigation, experiential learning, governance integration, community engagement, and interdisciplinary coordination. Ultimately, robust and resilient health systems are essential for effectively managing health crises, as they enable rapid disease surveillance, efficient resource allocation, and timely delivery of medical interventions to mitigate the spread and impact of outbreaks.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".