Knowledge and Practices of Women Attending Postnatal Consultations on Vaccination Against COVID-19 in the Health District of Sakal in 2022 (Senegal)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».