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Record W4412683638 · doi:10.1177/20543581251355024

Impacts of the COVID-19 Pandemic on Chronic Kidney Disease Care: Perspectives of Health Care Providers

2025· article· en· W4412683638 on OpenAlexafffundabout
Erin Hedin, David Nicholas, Andrew Mantulak, Marcello Tonelli, Rosslynn Zulla, R. Todd Alexander

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2025
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsThe King's UniversityWestern UniversityUniversity of CalgaryUniversity of Alberta
FundersKidney Foundation of Canada
KeywordsMedicinePandemicHealth carePsychosocialStaffingQualitative researchNursingFamily medicineDiseaseCoronavirus disease 2019 (COVID-19)PsychiatryInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Background: The COVID-19 pandemic significantly disrupted health care delivery; however, limited research attention has addressed the impacts of the pandemic specifically on renal-based health care. Objective: The aim of the study was to understand the health and psychosocial impacts of the COVID-19 pandemic on care delivery as well as on health care providers (HCPs). Design: A qualitative Interpretive Description approach was used. Setting: Participants were recruited from kidney care units in Alberta and Ontario, Canada. Participants: 46 interdisciplinary HCPs providing kidney care during the pandemic participated in the study. Participants comprised pediatric (n=9) and adult (n=37) HCPs. Methods: Qualitative focus groups were facilitated. They were conducted using Zoom (Version 6.3.11). Results: Research results indicated substantial strain on patients and HCPs during the COVID-19 pandemic, including mental health difficulties. For HCPs, moral distress was experienced from witnessing negative impacts of the pandemic and care shifts on patients. HCPs were further deleteriously affected by staffing shortages, facility limitations due to care interruptions and resource gaps in health and community systems. Conversely, participants identified concurrent benefits from pandemic-related shifts such as the rapid transition to virtual care. While virtual care imposed challenges for physical assessment of patients, patient convenience and increased access to HCPs were noted. With training, support and experience, HCPs and patients reported an appreciation for communication offered by virtual care where appropriate. Supportive unit leaders and peer support from HCP colleagues were viewed to make this difficult experience of the pandemic more manageable. HCPs drew strength in being able to support their patients despite the difficulties of the pandemic. Limitations: Firsthand experiences of patients and families were not offered in this article, although their perspectives were elicited in another arm of this study. A more specific focus on pediatric versus adult systems of care, and the impacts of the pandemic on various disciplines, would add depth to understanding the unique impacts of the pandemic on various areas of care and across disciplines. Conclusions: Study results call for the advancement of clinical care and heightened consideration of HCP needs in a pandemic. Included recommendations are ensuring sufficient resources, prudent use of communication technology, and optimizing support to HCPs.

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)0.017
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 score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0110.008
Scholarly communication0.0050.004
Open science0.0010.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.031
GPT teacher head0.370
Teacher spread0.339 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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