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Record W4323351175 · doi:10.1093/jcag/gwac036.188

A188 INFORMING IMMUNOCOMPROMISED POPULATIONS: AN EFFECTIVE AND EFFICIENT COVID-19 KNOWLEDGE TRANSLATION STRATEGY

2023· article· en· W4323351175 on OpenAlexaffabout
Joseph W. Windsor, Stephanie Coward, K Lee, A Specic, S Ghandeharian, Eric I. Benchimol, Gilaad G. Kaplan

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsHospital for Sick ChildrenCrohn's and Colitis CanadaUniversity of Calgary
Fundersnot available
KeywordsPandemicInfographicPopulationMedicinePublic healthGovernment (linguistics)Family medicineKnowledge translationHealth careCoronavirus disease 2019 (COVID-19)DiseaseMedical educationInfectious disease (medical specialty)NursingEnvironmental healthPathologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Background Throughout the COVID-19 pandemic, one of the major challenges was conveying expert health information, which was evolving rapidly, to confer population-level advice; this was especially relevant to at risk groups, such as those who are immunocompromised due to conditions like inflammatory bowel disease (IBD) or medications to manage disease. Purpose To provide sufficient information to those with IBD (>0.75% of the Canadian population or roughly 300,000 individuals) and their carers to allow self assessment of personal risks related to COVID-19. Method On March 17, 2020, Crohn’s and Colitis Canada (CCC) convened the COVID-19 & IBD Taskforce comprised of adult and pediatric gastroenterologists, IBD nurses, infectious disease experts, scientists, public health officials, communication and government relations experts, and patient advisors. The taskforce met weekly (later monthly) to synthesize rapidly evolving information on COVID-19 and personal risk assessment. Expert reviews of population-level recommendations were tailored to the IBD community and communicated through website FAQs and infographics; a public-oriented burden report with foci on additional special populations (e.g., pregnant people, pediatrics, seniors), IBD medications, and mental health and access to care during the pandemic; and through a moderated, online webinar series. The 1- to 2-hour webinar recordings were then curated into 3- to 5-minute video clips to answer specific questions and uploaded to CCC’s YouTube page. YouTube and website metrics show the continued efficacy of this strategy. Result(s) More than 24,778 households registered for the first 23 webinars, with more than one third registering for more than one webinar. As of April 1, 2021 (just after the 23rd webinar), there have been 54,136 views of the archived full (1- to 2-hour) webinars and a further 78,862 views of individual webinar segments (3- to 5-minute curated clips), for a total of 126,187 views. Additionally, traffic to the CCC website increased exponentially with 484,755 unique views to the COVID-19 web pages, viewed for up to 28.29 minutes. Since April 2021, after an additional seven webinars, these numbers have continued to swell to 33,243 (registrants), 81,370 (webinar views), 92,862 (segment views), and 810,156 (unique website views); this is demonstrative of the continued impact of the electronic knowledge translation strategy. Image Conclusion(s) While many within the IBD community were secluded during the early portions of the pandemic due to lockdown restrictions and public health advice not tailored to immunocompromised individuals, advice tailored to this community and presented through electronic methods proved to be an effective and efficient knowledge translation strategy. Please acknowledge all funding agencies by checking the applicable boxes below CCC Disclosure of Interest None Declared

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)0.082
metaresearch head score (Gemma)0.138
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.434

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0820.138
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0080.004
Science and technology studies0.0040.002
Scholarly communication0.0100.010
Open science0.0050.020
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0410.015

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 head0.045
GPT teacher head0.330
Teacher spread0.285 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2023
Admission routes2
Has abstractyes

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