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Record W4377136698 · doi:10.1002/nop2.1854

Disturbances of nursing students in internship during emerging disease COVID‐19 pandemic: A qualitative study

2023· article· en· W4377136698 on OpenAlexaff
Hossein Feizollahzadeh, Hadi Hassankhani, Masoomeh Barsaei

Bibliographic record

VenueNursing Open · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsSaskatchewan Polytechnic
Fundersnot available
KeywordsInternshipPandemicChecklistQualitative researchNursingCoronavirus disease 2019 (COVID-19)PsychologyMedical educationMedicineContent analysisDiseaseInfectious disease (medical specialty)Sociology

Abstract

fetched live from OpenAlex

AIM: The aim of the study was to examine the experiences of nursing students in internship during the COVID-19 pandemic. DESIGN: A qualitative study. METHODS: Purposeful sampling was conducted among undergraduate nursing students at Tabriz School of Nursing in November 2021. Students participated in 14 in-depth open-ended interviews and stated their experiences and opinions on internships during the COVID epidemic until full data saturation. Data analysis was performed using the conventional content analysis method. This study followed the Standards for Reporting Qualitative Research (SRQR) checklist. RESULTS: Findings were extracted and classified into five main categories, including a lack of facilities and equipment, psychological disturbances, physical risk, disturbances in education and learning activities and movement to continue clinical learning in the situation. CONCLUSION: Nursing students in clinical training during the COVID epidemic have experienced physical and mental health issues, as well as educational challenges. During an infectious disease epidemic period, education administrators should adopt appropriate strategies to protect students' health and facilitate their educational and learning activities.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.342
GPT teacher head0.628
Teacher spread0.286 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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