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Record W4386983431 · doi:10.1093/pch/pxad055.114

R3 (Resident Advocacy Project) Evaluation of a Novel COVID-19 Vaccine Consult Service

2023· article· en· W4386983431 on OpenAlexaboutno aff
Danielle Gibbs, Julia Orkin, Blossom Dharmaraj, Lise Cinq-Mars, Lindsay Clarke, Genevieve Russett, Donna Solomon, Allison Boyce, Patricia Beasley

Bibliographic record

VenuePaediatrics & Child Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisMedicineVaccinationReferralFamily medicinePopulationCoronavirus disease 2019 (COVID-19)Service (business)NursingMedical emergencyQualitative researchBusinessEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Rationale and Objectives Strengthening confidence in COVID-19 vaccination in the paediatric population promotes more children getting vaccinated, leading to fewer COVID-19 illnesses, hospitalizations and deaths. Paediatric providers play an important role in promoting dialogue on the risks and benefits of the COVID-19 vaccines for children. Caregivers who experience hesitancy toward the vaccines may lack access to quality medical information. Developed to close this gap, the SickKids COVID-19 Vaccine Consult Service (VCS) is a nurse-led, telephone consultation service for residents of Ontario with questions/concerns about COVID-19 vaccination for children. The VCS aims to address vaccine hesitancy and enhance vaccine uptake, and ensures that children and caregivers have equitable access to timely and reliable information regarding COVID-19 vaccines in a safe and judgement-free environment. The VCS has escalation pathways in place should further discussion with a physician be required. Appointments can be booked without a referral and discussions are held by telephone. The VCS supports families whose children require specialized accommodation for vaccination with appointments at SickKids and throughout Ontario. Project Description A retrospective review of telephone consults received by the VCS between October 1, 2021- April 1, 2022 was undertaken to evaluate the service utilizing descriptive methodology. Information was extracted from databases stored in RedCap/EPIC and was maintained by staff conducting the calls. Inductive Thematic Analysis was utilized to analyze qualitative data and codes were generated to inform themes. Feedback formation was collected via exit surveys and included details about who was calling, reasons for calling, if concerns had been addressed, and intention to vaccinate. Outcomes Parents were the majority of callers (96%) and 4% were patients themselves, grandparents or other. A total of 99% of the calls included general questions about the vaccine, 36% of calls were questions about children with underlying medical conditions (cardiac conditions being the most common), and 12% were allergy-related questions. 9% of calls required a consult with MD/NP. 83% of questions/concerns were resolved following the call. Further, the majority of those surveyed following the appointment expressed intent for vaccination. Safety and development, side effects, and doses/intervals were the most common reason callers described hesitancy with proceeding with vaccination. Discussion/Future Directions Providing caregivers with timely and reliable information regarding COVID-19 vaccines through the VCS has proven to be effective in reducing vaccine hesitancy and increasing vaccine uptake in the paediatric community. In the future, the VCS model can be utilized to provide education to caregivers on other routine childhood vaccines.

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)0.036
metaresearch head score (Gemma)0.048
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 score0.086
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0030.002
Open science0.0030.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.085
GPT teacher head0.404
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
Published2023
Admission routes1
Has abstractyes

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