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Record W4411846618 · doi:10.3899/jrheum.2025-0314.67

Engaging Community Pharmacy to Improve Seasonal Vaccination in Patients Living with Vasculitis

2025· article· en· W4411846618 on OpenAlexaffvenueabout
Stephen Williams, Aurore Fifi‐Mah

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicHeparin-Induced Thrombocytopenia and Thrombosis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePharmacyVaccinationVasculitisFamily medicineDermatologyIntensive care medicineInternal medicineImmunologyDisease

Abstract

fetched live from OpenAlex

Objectives Vasculitis refers to a group of autoimmune disorders causing inflammatory damage of blood vessels leading to end organ damage. Treating these conditions often requires therapeutic immunosuppression. This immunosuppression is associated with an increased risk of infection, a leading cause of mortality in vasculitis.[1] Vaccinations reduce the risk of certain infections but are underutilized.[2] Novel strategies to promote patient vaccination are crucial to reduce infections. In this study, we implemented a multidisciplinary approach involving community pharmacies to improve the rate of seasonal vaccination. Methods 103 patients > age 18 living with vasculitis seen in 1 tertiary center in Calgary, Alberta were recruited. Their vaccination status against COVID-19 and influenza was documented to establish a baseline. As part of a multidisciplinary vaccine promotion approach, we provided these patients with education on the importance of vaccination and contacted their pharmacy of choice to arrange vaccination if the patient was not up to date. The vaccination status of patients was then reviewed at the 16-month time point to determine subsequent vaccination rate. Results At baseline 15% of patients had received their seasonal influenza vaccination and 39% of patients had received >3 mRNA COVID-19 vaccinations. Following the multidisciplinary vaccine promotion intervention, the rates of vaccination for the 2023-2024 season increased to 49% for influenza vaccination and 65% for receiving >3 mRNA COVID-19 vaccines. Compared to the provincial rates for the same time period, our intervention resulted in 2x higher influenza vaccination rates and nearly 3x higher COVID-19 vaccination rates (Image 1). Additionally, a vaccine hesitancy survey was provided to better understand hesitant patient behavior. 47 patients agreed to provide their reasoning for not completing their vaccination series. Timing (making time, remembering to go) was the major obstacle noted by 31/47 (66%) while mistrust of vaccines (lack of belief in efficacy, potential to cause harm) was expressed by 19% (9/47). Of these patients 83% (39/47) stated they would receive vaccination on-site at the time of their next rheumatology appointment if it were offered by a health professional. Image 1: Summary of Findings Conclusion Engaging patients’ community pharmacy to promote and administer seasonal vaccination was an effective preventative care initiative. There were improved vaccination rates for influenza and COVID-19 and our final vaccination rates were superior to provincial rates over the same time period. As a QI initiative, this study highlights an effective multidisciplinary approach to improving patient vaccination. Such approaches are especially salient and increasing burden on rheumatologists to ensure risk reduction in multiple domains. [1.] King C. Best Pract Res Clin Rheumatol 2018;32(1):125-36. [2.] Furer V. RMD Open 2019;5(2):e001041. Practice Reflection Award

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)0.001
metaresearch head score (Gemma)0.005
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.301
Teacher spread0.284 · 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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Citations0
Published2025
Admission routes3
Has abstractyes

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