Knowledge, attitude and disinformation regarding vaccination and immunization practices among healthcare workers of a third-level paediatric hospital
Notice bibliographique
Résumé
BACKGROUND: Vaccination represents one of the most effective means of preventing infections for the population and for the public health in general. Recently there has been a decline in vaccinations, also among healthcare workers (HCWs). The aim of the study is to detect the knowledge, skills, attitudes and barriers of HCWs regarding vaccinations in a tertiary children's hospital in order to support clinical management in immunisation practices. METHODS: An observational study was conducted on 255 subjects over a period of 8 months. The 31-item questionnaire considered profession, level of instruction and different ages. It included questions taken from a questionnaire used for a Canadian research and one used by the Bellinzona hospital. A 4-point Likert scale and closed-ended questions were used. A confidence interval of 95%, p value ≤ 0.05, Chi-square, ANOVA and the Kruskal-Wallis test were considered. RESULTS: In the last 5 years less than one third of the sample were vaccinated against flu. 77.8% (n.130) of nurses and 45.8% (n.19) of doctors were not vaccinated (p < 0.0001). As for risk perception, 51.5% of nurses and 90.6% of doctors believe that their risk of contracting influenza is greater than that of the general population. In relation to the injection site, in all the age ranges there was a high level of knowledge except for those aged over 61 who responded incorrectly. Doctors were more prepared (p < 0.0001). 50% of the sample used internet only as a source of information for vaccines. Generally, scientific sources were used infrequently. The higher the education level, the more frequent the utilisation of trustworthy scientific resources and literature. (p = 0.0002). CONCLUSIONS: In line with the attitude observed in recent years, nurses are not inclined to get vaccinated themselves although they agree to having their children vaccinated. HCWs have a good level of knowledge about vaccines and immunisation practices. With the nurses we found that the higher the education level, the greater the knowledge about vaccines which leads to the conclusion that low levels of adherence are not due to a lack of knowledge, but rather, to a low perception of risks. Hence the need to strengthen the vaccination strategies inside the companies.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,004 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».