Evaluation of Vaccination Coverage Against COVID-19 Among Postpartum Women at the Gaspard Kamara Health Center in Senegal in 2020
Bibliographic record
Abstract
The COVID-19 pandemic has shaken the world and the rapid delivery of vaccines against the virus has raised many questions. Doubts have been raised about the effectiveness and safety of the vaccine in pregnant and breastfeeding women, hence the need to assess vaccination coverage of postpartum women at the Gaspard Kamara health center. We therefore carried out a cross-sectional analytical study from May 31 to June 28, 2022 on all women who gave birth at the Gaspard Kamara health center. Exhaustive recruitment was carried out and the data were analyzed using the SSP software version 2022. In total, 235 women participated in the study, with a mean age of 28.22 ± 5.96 years and extremes 16 and 44 years old. More than half of the patients (68.94%) had given birth vaginally. Those with secondary education accounted for 72.34%. Less than a quarter of women (7.23%) tested positive for COVID19 and 96.60% of them received information on the fight against COVID19, with the media being their main source of information. Less than half (40.43%) knew the vaccines available in Senegal, Johnson & Johnson being the best known. The majority of women (82.55%) believe in the effectiveness of vaccination to eradicate the disease. Age (30 years), lack of information on the fight against COVID19, lack of knowledge of the risks of serious forms in FE, knowledge of the availability of the vaccine in Senegal and vaccination of the spouse were statistically associated with vaccination against COVID19 among women in this country. study. Conclusion: The results of this study show the need to strengthen awareness and communication on the effectiveness and safety of vaccines among pregnant women, particularly those under 30 years of age. Let women know that they can be vaccinated at any age and during any trimester of pregnancy and breastfeeding.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".