Knowledge and Practices of Women Attending Postnatal Consultations on Vaccination Against COVID-19 in the Health District of Sakal in 2022 (Senegal)
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic remains a public health problem despite the lulls between waves. Achieving the goal of broad vaccination coverage is of paramount importance, particularly for populations at risk of severe forms of COVID-19. Hence the interest in studying vaccination among women attending post-natal consultations in the Sakal Health District. Methodology: A cross-sectional, descriptive and analytical study was conducted from 28 June to 27 October 2022. The study population consisted of women who had given birth and/or come for a postnatal consultation in the Sakal health district. Data were analysed using R 4.2 software. Results: The mean age of the women surveyed was 26.56±6 years, with extremes of 17 to 43 years. The median age was 25 years. The age groups most represented were [20-30], with a proportion of 50.6%. More than 80% of these women were uneducated (30.4%) or had no more than primary education (51.4%). A further 17% had completed secondary education, and only 1.2% had completed higher education. A quarter of these women had an income generating activity (15.2%). Almost ¾ of respondents had a television at home (70.2%) and a telephone (73.1%). Of those who had a phone, more than half (65.1%) had a smartphone, almost all of which had access to social networks (96.3%). The types of social network whose use was significantly associated with vaccination against COVID-19 were: WhatsApp (p=0.004), Facebook (p=0.008), TikTok (p=0.021) and YouTube (p=0.015). YouTube was the source of information with a statistically significant association with vaccination against COVID-19 (p=0.015). The next three elements, i.e. knowledge of previous COVID-19 infection (p=0.01), knowledge of someone who had been infected with the disease (p=0.042) and knowledge of available COVID-19 vaccines (p<0.001) were significantly linked to vaccination against COVID-19. Women who were aware of the available COVID-19 vaccines were 8.33 times more likely to be vaccinated. All the women surveyed were married, 25.7% of whom had been vaccinated, and those whose spouses had been vaccinated were 7.3 times more likely to be vaccinated in turn (p<0.001). Conclusion: The results of this study demonstrate the need to raise awareness of COVID-19 vaccination among pregnant women, with the full involvement of the spouse. Priority should be given to raising awareness of the seriousness of COVID-19 in pregnant women, the benefits of being vaccinated and, of course, reassurance about the safety of the available COVID-19 vaccines.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".