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Record W3198513184 · doi:10.1097/ncq.0000000000000590

Lived Experience of Medicine Nurses Caring for COVID-19 Patients

2021· article· en· W3198513184 on OpenAlexaffabout
Natasha Mohammed, Hannah Lelièvre

Bibliographic record

VenueJournal of Nursing Care Quality · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsThematic analysisPandemicNursingCoronavirus disease 2019 (COVID-19)Health careQualitative researchPatient experienceMEDLINELived experienceMedicinePsychologyInfectious disease (medical specialty)Sociology

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic has overwhelmed health care systems globally. To understand how health care systems can best support frontline health care providers caring for patients in similar situations, it is necessary to gain insights into their experience. PURPOSE: This quality improvement study explored the lived experience of Canadian frontline medicine nurses caring for COVID-19 patients during the first wave of the pandemic. METHOD: A qualitative interpretive phenomenological approach was conducted. Forty-three eligible nurses participated in semistructured interviews and online surveys. Full transcription and thematic content analysis were performed. RESULTS: Three overarching themes were deduced: (1) a traumatic experience, (2) living through the experience, and (3) achieving transcendence. CONCLUSION: Several recommendations were identified. These recommendations aim to aid health care systems in emergency preparation planning and future pandemic responses while supporting frontline health care providers' resilience and well-being.

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 score0.126
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.299
GPT teacher head0.584
Teacher spread0.285 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

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