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Enregistrement W3202457835 · doi:10.1136/archdischild-2021-rcpch.380

1079 Implementation of communication strategy to improve information distribution and patient care during the covid-19 pandemic

2021· article· en· W3202457835 sur OpenAlexaff
Ankita Sahni, Sahana Rao

Notice bibliographique

RevueAbstracts · 2021
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealthcare Quality and Satisfaction
Établissements canadiensUniversity Hospital Foundation
Organismes subventionnairesnon disponible
Mots-clésMedicinePandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Distribution (mathematics)VirologyOutbreakPathologyDiseaseInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

<h3>Background</h3> This project was undertaken at a large tertiary teaching hospital involving members of the multi-professional team involved in patient care during the COVID-19 pandemic. Early on, we realised that much of our information distribution relied on emails and face-to-face meetings. With rapidly changing guidelines and recommendations, quantity of information to distribute became overwhelming. Staff were receiving multiple, daily trust-wide and department-specific emails. There was huge information overload, resulting in miscommunication. <h3>Objectives</h3> 1. To provide up-to-date information that has been appraised for accuracy, relevance and importance 2. Increase effectiveness in information distribution - identify relevant recipients, timely distribution, minimising information overload, and creating a repository for reference <h3>Methods</h3> Our QI methodology is based on the model for improvement framework and PDSA cycles. PDSA cycle 1: Identifying stakeholders, and a preferred method of communication Stakeholders were identified and engaged. Baseline data was taken from the trust's internal communication survey data We agreed on a trial information distribution via an intranet page PDSA cycle 2: Implementation of the Covid-19 intranet page Paediatric Consultant led the design of the webpage, including content, location and structure. The webpage was reviewed using Dalhouse university criteria. Informal feedback was regularly sought from stakeholders to upkeep the webpage. A formal survey was could not be completed at 3 months due to staff redeployment. PDSA cycle 3: Improving awareness of the intranet page - in progress. The intranet page was advertised in induction for new staff and disseminated in the monthly staff bulletin. Survey was performed at 6 months to collect quantitative and qualitative data to assess staff use and satisfaction <h3>Results</h3> PDSA cycle 1: We identified staff bulletins, emails, intranet and team meetings as staff's preferred methods of communication. 51% of respondents reporting using the intranet daily, and a further 29% using every few days. 90% rated the intranet as a useful resource. PDSA cycle 3: Survey data showed that 75% reported accessed the website, with 61% of these using it on a weekly basis. It was mostly accessed for information for staff, PPE guidance and testing policies. The website was rated highly for accuracy, ease of access, useful and up-to-date information. All topics were rated useful and respondents were highly likely to recommend it to other colleagues. Qualitative responses were assessed with word clouds. The 3 main words were as follows: key successes - easy, organised, relevant; areas for improvement: awareness, reminders, layout. Of the 25% that did not use the webpage, all cited lack of awareness as the reason. <h3>Conclusions</h3> These were unprecedented times with rapidly changing guidelines. Creation and distribution of easily accessible up-to-date information to colleagues was increasingly important. Creating a central point of reference worked well for a large hospital where the staff base changes regularly and already have saturated email inboxes. Ensuring that information was aimed at all members of the MDT provided streamlined and unified information.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,492
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,087
Tête enseignante GPT0,467
Écart entre enseignants0,380 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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