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Record W4409448071 · doi:10.1159/000545782

Experiences of Care Providers Working in Long-Term Care during the COVID-19 Pandemic

2025· article· en· W4409448071 on OpenAlexaffabout
David Nicholas, Rosslynn Zulla, Jennifer Hewson, Navjot Kaur Virk, Jenna Naylor

Bibliographic record

VenueGerontology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsThe Sharp FoundationUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsPandemicNursingLong-term careStressorFront lineService providerFocus groupWork (physics)Qualitative researchPsychologyCoronavirus disease 2019 (COVID-19)Organizational cultureService (business)BusinessMedicinePublic relationsMarketingPolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: In this qualitative study, care providers from long-term care homes were interviewed to explore how they experienced, coped with, and adapted to care shifts during the COVID-19 pandemic. METHODS: Thirteen multidisciplinary care providers and 24 supervisory and administrative staff participated in either a focus group or individual interview between July 2021 and February 2022. Participants were front-line care providers in 5 urban long-term care homes in western Canada. RESULTS: Care providers described negative impacts on residents and family members related to service delivery, restricted visiting, and quarantining protocols. They also identified negative impacts they experienced as care providers including fear and uncertainty, exhaustion, concerns about care provision, lower morale, and job self-efficacy. Buffers to stress comprised working as an integrated team and organizational support. Opportunities for growth and development and being adaptive were also described. Recommendations focused on organizational pandemic readiness and the importance of holistic care. CONCLUSION: These findings highlight the need to proactively ensure a supportive infrastructure, wellness-promoting work culture, and a sustainable resource plan to help care providers pivot and adapt in a pandemic.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.066
GPT teacher head0.427
Teacher spread0.362 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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