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Record W4402668979 · doi:10.1111/jan.16468

A Qualitative Analysis of the Functions of Primary Care Nurses in <scp>COVID</scp> ‐19 Vaccination

2024· article· en· W4402668979 on OpenAlexafffundabout
Rhiannon Lyons, Maria Mathews, Dana Ryan, Lindsay Hedden, Julia Lukewich, Emily Gard Marshall, Paul Gill, Jennifer E. Isenor, Ruth Martin‐Misener, Jamie Wickett, Donna Bulman, Émilie Dufour, Leslie Meredith, Sarah Spencer, Crystal Vaughan, Judith Belle Brown

Bibliographic record

VenueJournal of Advanced Nursing · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsDalhousie UniversityMemorial University of NewfoundlandSimon Fraser UniversityWestern University
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsOutreachVaccinationThematic analysisNursingPrimary carePandemicMedicineQualitative researchFamily medicineCoronavirus disease 2019 (COVID-19)PsychologyPolitical scienceVirologyDiseaseSociology

Abstract

fetched live from OpenAlex

AIM: To describe vaccination roles of primary care nurses during the COVID-19 pandemic in Canada. DESIGN: This analysis was part of a larger mixed-methods case study. METHODS: We conducted semi-structured qualitative interviews from May 2022 to January 2023 with primary care nurses across four provinces: British Columbia, Ontario, Newfoundland and Labrador, and Nova Scotia. We asked participants to describe their roles during various stages of the pandemic, facilitators and challenges encountered and possible roles that nurses could have played. We used thematic analysis and analysed codes relevant to vaccination. RESULTS: We interviewed a total of 76 nurses and identified four key functions of primary care nurses' roles in COVID-19 vaccination: (1) education, (2) vaccine administration, (3) outreach and (4) advocacy. Themes outlined nurses' roles with respect to patient education, addressing vaccine hesitancy, partaking in vaccination roles outside of regular primary care practice and supporting accessibility in COVID-19 vaccination. Specific tasks varied by nursing professions. CONCLUSION: Primary care nurses fostered trust through existing patient-provider relationships to enhance roles and activities related to education, outreach and advocacy in COVID-19 vaccination. Some COVID-19 vaccine-related roles were more easily integrated into primary care, whereas others competed with routine primary care roles. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: Findings highlight the vital contributions of primary care nurses towards COVID-19 vaccination efforts in Canada. Leveraging nursing expertise can enhance future pandemic response efforts and improve patient care by addressing barriers to vaccination and promoting equitable access to vaccination services. IMPACT: This study addresses a knowledge gap by describing the vaccination-related roles of primary care nurses during the pandemic. Findings illustrate that nurses demonstrated adaptability through their engagement in vaccine education, administration, outreach and advocacy. This research informs resource allocation, policy development and workforce planning for future vaccination efforts during a pandemic response. REPORTING METHOD: The authors have adhered to the Standards for Reporting Qualitative Research (SRQR) guidelines included in the Empirical Research Qualitative reporting method. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. WHAT DOES THIS PAPER CONTRIBUTE TO THE WIDER GLOBAL CLINICAL COMMUNITY?: Provides insight into the pivotal roles of primary care nurses during the COVID-19 vaccination efforts in Canada, highlighting their diverse contributions towards education, vaccine administration, outreach and advocacy. Offers implications for future pandemic planning by informing resource allocation, policy development and workforce planning for vaccination efforts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.398
Teacher spread0.371 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2024
Admission routes3
Has abstractyes

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