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Record W4213372313 · doi:10.1093/jcag/gwab049.095

A96 RAPID IMPLEMENTATION OF AN EVIDENCE-BASED, VIRTUAL COVID-19 VACCINE EDUCATION CLINIC AT NOVA SCOTIA COLLABORATIVE INFLAMMATORY DISEASE CLINIC (NSCIBD)

2022· article· en· W4213372313 on OpenAlexaffabout
H Komeylian, Jennifer Jones, Michael J. Stewart, Courtney Heisler, K Phalen-Kelly, Barbara Currie

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsQueen Elizabeth II Health Sciences CentreHealth Sciences CentreDalhousie University
Fundersnot available
KeywordsMedicineObservational studyFamily medicineSession (web analytics)VaccinationNova scotiaDescriptive statisticsIntervention (counseling)Medical educationNursingInternal medicineImmunology

Abstract

fetched live from OpenAlex

Abstract Background Rapid adaptation of clinical management as well as policy decisions in relation to implementation of COVID-19 vaccination programs for persons living with IBD has been required throughout the pandemic. Aims To meet the need for 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 An observational, cross sectional, implementation-effectiveness study was conducted at the NSCIBD program between April and July, 2021. The educational intervention consisted of a standardized evidence-based letter describing risks and benefits of COVID-19 vaccine emailed to patients in advance of a virtual clinic appointment. Virtual appointments were offered to all patients contacting the NSCIBD program with questions or concerns about vaccination. During these virtual visits standardized, evidence-based information was provided by a gastroenterologist (n=2) or IBD nurse practitioners (n=2) and patients were provided with an opportunity to address specific disease and treatment related concerns. Following the session, a link to an anonymous questionnaire was distributed via email to evaluate key implementation metrics including satisfaction, appropriateness, usefulness, perceived impact on knowledge and vaccine hesitancy, and recommendations for improvement. Data analysis was descriptive. Group means were expressed as proportions for categorical variables and means for numerical variables. Results A total of 298 patients participated in a virtual patient education session of which 265 provided a valid email address and invited to participate in the on-line survey. The response rate was 49% (131/265). Before the session, 48.9% (64/131) expressed vaccine hesitancy. Twenty-six percent (35/131) expressed concerns relating to risks versus benefits of COVID-19 vaccines. Ninety-one percent (119/131) of respondents found the education program helpful. The proportion of those willing to get vaccinated rose from 61% (pre) to 86.3% (post). Only 1.5% (2/131) indicated they would not get vaccinated. Seventy-seven percent (101/131) found the written and virtually administered educational content to be satisfactory. Eighty-eight percent (115/131) of respondents were willing to participate in similar types of virtual education offerings in the future. Conclusions Implementation of an evidence-based, multidisciplinary, virtual COVID-19 vaccination education intervention 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. Funding Agencies None

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.278

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 head0.025
GPT teacher head0.344
Teacher spread0.319 · 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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

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