Analysis of barriers in effective immunization against COVID 19 using F-DEMATEL
Bibliographic record
Abstract
India had broken all the records and counts of confirmed COVID-19 cases per day and daily death toll reached over thousands. India is way far from other developed nations in the number of vaccine doses per 100 population. Although vaccination is an effective measure to be followed to overcome this grave situation, still certain misconceptions and rumors throughout the country have pulled a decent part of the population from being vaccinated. Another big challenge is production and supply of vaccines to meet the demand. COVID-19 pandemic will not end until the entire population gets vaccinated that would protect them from this deadly disease. Therefore, this paper aims at clearly identifying the factors and subsequently prioritizing them as barriers in effective immunization against COVID-19 in India following multi-criteria decision making (MCDM) technique. In this study, a fuzzy decision-making trail and evaluation laboratory (F-DEMATEL) approach is applied for understanding the contextual relationship among the barriers for effective immunization against COVID-19. The methodology is followed in a fuzzy environment to address the issue of uncertainty in the data gathered. The result suggests that the ‘Misinformation/ Misconceptions/ Lack of vaccine education in underserved communities’, ‘Lack of information regarding a vaccination center close to home’, ‘Difficulties in getting appointments’, ‘Supply chain issues in the distribution of vaccine’, and ‘Lack of access for marginalized communities’ are the important barriers in effective immunization against COVID-19. Recommendations have been made to overcome this situation and help to immunize the population and drag COVID-19 down to earth.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".