Learning from the COVID-19 pandemic for future epidemics and pandemics preparedness and response in Guinea: Findings from a scoping review
Bibliographic record
Abstract
November 2021 and lessons for future pandemic preparedness and response. We conducted a scoping review using three main data sources: policy documents, research papers and media content. For each of these data sources, a specific search strategy was applied, respectively national websites, PubMed and the Factiva media database. A content analysis was conducted to assess the information found. We found that between January 2020 and November 2021, the response to the COVID-19 pandemic can be divided into five phases: (1) anticipation of the response, (2) a sudden boost of political actions with the implementation of strict restrictive measures, (3) alleviation of restrictive measures, (4) multiple epidemics period and (5) the COVID-19 variants phase, including the strengthening of vaccination activities. This study provides several learning points for countries with similar contexts including: (1) the necessity of setting up, in the pre-epidemic period, an epidemic governance framework that is articulated with the country's health system and epidemiological contexts; (2) the importance of mobilizing, during pre-epidemic period, emergency funds for a rapid health system response whenever epidemics hit; (3) each epidemic is a new experience as previous exposure to similar ones does not necessarily guarantee population and health system resilience; (4) epidemics generate social distress because of the restrictive measures they require for their control, but their excessive securitization is counterproductive. Finally, from a political point of view, decision-making for epidemic control is not always disinterested; it is sometimes rooted in political computations, and health system actors should learn to cope with it while, at the same time, safeguarding trusted and efficient health system responses. We conclude that health system actors anticipated the response to the COVID-19 pandemic and (re-) adapted response strategies as the pandemic evolved in the country. There is a need to rethink epidemics governance and funding mechanisms in Guinea to improve the health system response to epidemics.
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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 | 0.028 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".