Workplace violence against Bangladeshi registered nurses: A survey following a year of the COVID‐19 pandemic
Notice bibliographique
Résumé
AIMS: To investigate the prevalence of workplace violence and its associated factors among Bangladeshi registered nurses. BACKGROUND: Workplace violence is prevalent among nurses, particularly in developing countries. However, the issue has never been examined in Bangladeshi nurses. METHODS: Between February 26 and July 10, 2021, this cross-sectional survey involving 1264 registered nurses was conducted. Workplace violence was determined by the Workplace Violence Scale (WVS). A multivariable logistic regression model was fitted to find the factors associated with workplace violence. This study complies with the EQUATOR (STROBE) checklist. RESULTS: Of the 1264 nurses, 885 (70%) nurses reported being exposed to workplace violence in the previous year. Three hundred twenty-four (324; 25.6%) nurses reported physical violence, whereas 902 (71.4%) nurses reported nonphysical violence. According to the multivariable logistic regression model, male nurses, nurses in the Sylhet division, emergency department nurses, nurses working extended hours, and non trained nurses to tackle workplace violence were prone to physical violence. Furthermore, public hospital nurses and non trained nurses to tackle workplace violence were more likely to be exposed to nonphysical violence. Nurses who had not been exposed to workplace violence were satisfied with their current job, but those who had been exposed to workplace violence were dissatisfied and intended to leave their current job. CONCLUSIONS AND IMPLICATIONS FOR NURSING AND HEALTH POLICY: High prevalence of workplace violence underscores nurses' current working conditions, which are particularly poor in public hospitals and emergency departments. Moreover, the COVID-19 pandemic put unprecedented pressure on the whole healthcare system and caused various difficulties for healthcare workers. To develop a zero-violence practice environment, health authorities should implement policy-level interventions. Healthcare staff should be guided to deal more successfully with patients and coworkers to create a positive working environment.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,003 |
| 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 | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».