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

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

Bibliographic record

VenueAbstracts · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Satisfaction
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsMedicinePandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Distribution (mathematics)VirologyOutbreakPathologyDiseaseInfectious disease (medical specialty)

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.467
Teacher spread0.380 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
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