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Record W4206978037 · doi:10.1093/ecco-jcc/jjab232.514

P387 Rapid Implementation of an Evidence-Based, Virtual COVID-19 Vaccine Education Clinic at the Nova Scotia Collaborative Inflammatory Bowel Disease Clinic (NSCIBD) Program

2022· article· en· W4206978037 on OpenAlexaffabout
H Komeylian, Michael J. Stewart, Barbara Currie, K Phalen-Kelly, Courtney Heisler, Jennifer Jones

Bibliographic record

VenueJournal of Crohn s and Colitis · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsMedicineObservational studyVaccinationFamily medicinePandemicIntervention (counseling)Nova scotiaSession (web analytics)DiseaseCoronavirus disease 2019 (COVID-19)NursingImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background The COVID-19 global pandemic has been associated with significant morbidity and mortality. Rapid adaptation of approaches to clinical management as well as policy decisions in relation to implementation of vaccination programs for persons living with IBD has been required throughout the pandemic. To meet the sudden demand of large scale public health-mandated COVID-19 vaccine education for patients living with IBD in Nova Scotia a novel, evidence-based, virtual COVID-19 vaccine educational intervention was developed, implemented, and evaluated. Methods This was a prospective, observational, cross sectional, implementation-effectiveness study conducted at the NSCIBD program between April-July, 2021. The educational intervention consisted of a standardized email outlining evidence relating to risks and benefits of COVID-19 vaccinations. The intervention was offered to all patients contacting the NSCIBD program with questions or concerns about the vaccine. During one-on-one virtual visits, standardized and evidence-based information was provided by a gastroenterologist or IBD nurse practitioner. Following the session, an anonymous questionnaire (NoviSurvey) evaluated key implementation metrics including satisfaction, appropriateness, usefulness, perceived impact on knowledge and vaccine hesitancy, willingness to participate in future sessions, and recommendations for improvement. Descriptive analyses were conducted, with group means expressed as proportions for categorical variables and means for numerical variables. Results A total of, 265 patients were invited to participate in the online survey, with a response rate of, 49% (131/265). Before the session, 48.9% (64/131) expressed COVID-19 vaccine hesitancy and, 26% (35/131) expressed concerns relating to risks versus benefits of COVID-19 vaccines. Ninety-one percent (119/131) of respondents found the education program to be helpful and, 92% (121/131) indicated there was no information perceived to be lacking from the session. Following the intervention, the proportion of those willing to get vaccinated rose from, 61% to, 86.3%. Only, 1.5% (2/131) indicated that they would likely not get vaccinated. Most participants (77%, 101/131) found the written and virtually administered educational content to be satisfactory and, 88% (115/131) were willing to participate in similar virtual education offerings in the future. Conclusion Implementation of an evidence-based, multidisciplinary COVID-19 vaccination education intervention delivered using a virtual platform was perceived to be feasible, acceptable, and effective by IBD patients. Further research on innovative, evidence-based, multidisciplinary educational interventions and the impact of these interventions on IBD clinical outcomes are needed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.034
GPT teacher head0.395
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), 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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Citations1
Published2022
Admission routes2
Has abstractyes

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