MétaCan
Menu
Back to cohort
Record W4392818516 · doi:10.1177/08980101241237103

A Journey of Uncertainty: Learned Lessons From the Lived Experiences of Nurses in Kuwait Taking Care of COVID-19 Patients in the Early Pandemic

2024· article· en· W4392818516 on OpenAlexaff
Muna Alshammari, Khaldoun Aldiabat

Bibliographic record

VenueJournal of Holistic Nursing · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsCape Breton University
Fundersnot available
KeywordsPandemicAmbivalenceCoping (psychology)Coronavirus disease 2019 (COVID-19)NursingQualitative researchHealth carePsychologyDiseaseMedicineInfectious disease (medical specialty)SociologyPsychiatryPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Purpose of the Study: This study explored the experiences of nurses in Kuwait who worked with COVID-19 patients during the first wave of the disease. Study Design: This was a qualitative descriptive study. Methods Used: In-depth interviews were conducted with seven (7) nurses who worked in intensive care units between September 2020 and March 2021. Findings: The experiences of Kuwait nurses in COVID-19 care showed an evolving journey of dealing with a strange and complex disease. With little known about the disease, the nurses approached COVID-19 care with uncertainty and ambivalence, unsure of where this journey would look like. Four themes emerged from the data and they included (1) from challenges to coping, (2) focusing on good health throughout the pandemic, (3) navigating through scarce resources and power dynamics, and (4) a multi-dimensional burden. Conclusions: Despite the difficulties encountered, supportive systems such as the availability of medical supplies, and support from superiors, colleagues, the community, and families, helped Kuwait nurses to cope with the stresses of an early COVID-19 pandemic while providing care. This approach takes a holistic stance to care for both patients and the nurses working in an epidemic setting.

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 score0.174
Threshold uncertainty score0.999

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 head0.202
GPT teacher head0.500
Teacher spread0.298 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Holistic NursingSame topicCOVID-19 and Mental HealthFrench-language works237,207