MétaCan
Menu
Back to cohort
Record W4410478716 · doi:10.1155/jonm/6634676

The Implementation of Infection Prevention and Control Procedures in Primary Care During the COVID‐19 Pandemic: A Qualitative Study of Nursing Roles

2025· article· en· W4410478716 on OpenAlexafffundabout
Samina Idrees, Maria Mathews, Lindsay Hedden, Julia Lukewich, Emily Gard Marshall, Kelly Kean, Rhiannon Lyons, Jamie Wickett, Leslie Meredith, Dana Ryan, Sarah Spencer, Émilie Dufour, Paul Gill

Bibliographic record

VenueJournal of Nursing Management · 2025
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsNewfoundland and Labrador Centre for Applied Health ResearchDalhousie UniversityGovernment of Newfoundland and LabradorMemorial University of NewfoundlandSimon Fraser UniversityWestern University
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsThematic analysisPandemicNursingWorkloadQualitative researchMedicinePrimary careHealth carePsychologyCoronavirus disease 2019 (COVID-19)Family medicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

Introduction: During the COVID‐19 pandemic, primary care practices felt poorly supported by existing infection prevention and control (IPAC) guidelines, which focused primarily on acute care facilities. This issue was further complicated by insufficient provision of personal protective equipment in primary care settings, which limited clinic capacity and the ability of primary care to provide in‐person services. Nurses play an integral role in the implementation of IPAC procedures and the provision of ongoing primary care during a health crisis; however there is limited literature related to nurses’ roles in the enactment of IPAC procedures in primary care settings. This paper aims to describe primary care nurses’ experiences and roles in implementing IPAC during the COVID‐19 pandemic. Design: Qualitative analysis of interviews as part of a larger mixed methods case study. Methods: We conducted semistructured qualitative interviews with primary care nurses across four Canadian regions in the provinces of British Columbia, Ontario, Nova Scotia, and Newfoundland and Labrador. During the interviews, we asked participants to describe the roles they enacted during the various stages of the pandemic, any facilitators and challenges they encountered, and the potential roles that nurses could have played. We employed a thematic analysis approach, and, for the purposes of this paper, we analyzed themes relevant to the implementation of IPAC. Results: We interviewed 76 nurses across the four regions and identified two overarching themes: (1) nurse‐led transformation of clinic operations and (2) impact on workload. Primary care nurses developed and implemented IPAC policies, educated staff, and made critical decisions about patient care, often out of necessity and ahead of regional guidelines. In addition, nurses adapted workflows, managed supplies, and balanced in‐person and virtual care to protect both patients and staff from COVID‐19 exposure. Conclusion: Despite the additional responsibilities and challenges that nurses faced in response to evolving guidelines, their IPAC efforts were pivotal in maintaining primary care clinic operations during the pandemic. The findings from this study underscore primary care nurses’ capacity to adapt and apply evidence‐based practices and demonstrate the need for better pandemic planning to support primary care. IPAC guidance documents, suitable for primary care settings and informed by experiences from the COVID‐19 pandemic, should be included in future pandemic plans. Implications for Nursing Management: Our findings highlight the need for stronger institutional support and preparedness for primary care nurses during a pandemic. Nursing management should ensure that IPAC responsibilities are explicitly recognized within primary care nursing roles and supported through ongoing training, resource allocation, and standardized protocols. Proactively integrating IPAC into primary care practice and strengthening these supports will enhance future health crisis preparedness while mitigating nurse burnout and promoting sustainable workforce capacity.

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)0.014
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0130.013
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.504
Teacher spread0.441 · 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 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

Citations2
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Nursing ManagementSame topicCOVID-19 and healthcare impactsFrench-language works237,207