A preliminary study on exploring the critical success factors for developing COVID-19 preventive strategy with an economy centric approach
Bibliographic record
Abstract
Purpose In the wake of COVID-19, most of the countries at present, are in a dilemma whether to extend lockdown at the cost of economy or to improve the hard-hit economy by lifting the lockdown. It is indicated by the reputed organizations and medical fraternity that corona will stay here for a longer period contrary to the earlier assumptions. Hence the purpose of this study is to suggest a strategy which balances both preventive measures and economic losses to control the pandemic. Design/methodology/approach There is a need for the identification of the critical success factors (CSFs) for developing COVID-19 preventive strategies to control the pandemic with an economy-centric approach. Findings The six CSFs identified are “Effective communication”, “Social distancing”, “Adopting new technology”, “Modify the rules and regulation at workplace”, “Sealing the borders of the territory” and “Strong leadership and government control”. Research limitations/implications The study has a vital contribution to literature as no previous study has identified CSFs for developing COVID-19 preventive strategies while focusing on the economy. Practical implications Further, these identified CSFs are helpful in medium and longer-term planning which is required to rebalance and re-energize the economy following this epidemic crisis. Originality/value The study has given a model that depicts the cause and influence relationship between the key factors in the system under question. The importance of study increases many fold, as resources are limited and the outcome of the study could be used to channelize the resources effectively.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".