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Record W4225829564 · doi:10.6018/eglobal.502991

Personal de enfermería contagiado por COVID-19: condiciones de trabajo y sus factores asociados en tres hospitales de Lima-Perú

2022· article· en· W4225829564 on OpenAlexaboutno aff
Orfelina Arpasi Quispe, Gaby Sonia Chávez Zegarra, Lili Albertina Fernández Molocho, Willy Jhon Medina Bacalla, Zoila Esperanza Leitón‐Espinoza, Vilanice Alves de Araújo Püschel, Jack Roberto Silva Fhon

Bibliographic record

VenueEnfermería Global · 2022
Typearticle
Languageen
FieldPsychology
TopicStress and Burnout Research
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)MedicineNursingUniversity hospitalDemographyHumanitiesDiseaseFamily medicineSociology

Abstract

fetched live from OpenAlex

Objective: To determinate the prevalence of COVID-19 in the nursing personnel, working conditions, and their associated factors in three hospitals in Lima-Peru.Method: A quantitative, descriptive cross-sectional study of the web survey was carried out between November 2020 and February 2021 with 495 nursing personnel members from the three public hospitals in Lima. Results: 63% of the participants were from the Guillermo Almenara Irigoyen National Hospital, 20% from the San Isidro Labrador Hospital, and 17% from the II Vitarte Hospital. The prevalence of COVID-19 in the nursing personnel was 47.3%. When analyzing the demographic characteristics of the participants, a significant association of the education variable with the disease was evidenced (OR=1.50, p =0.04), showing that those with higher education are 1.5 times more likely to develop COVID-19 compared to those with postgraduate studies; the variables related to working conditions and risk factors did not show a significant association (p>0.05) with the disease.Conclusions: Except for education, there is insufficient evidence to affirm a significant association of COVID-19 with demographic characteristics, work-related variables, and those considered a risk. This research makes significant contributions to nursing practice, research, and teaching. Objetivo: Determinar la prevalencia de la COVID-19 en el personal de enfermería, las condiciones de trabajo y sus factores asociados en tres hospitales de Lima-Perú.Método: Estudio cuantitativo, descriptivo de corte transversal del tipo web-survey realizado entre los meses de noviembre 2020 a febrero de 2021 con la participación de 495 integrantes del personal de enfermería de tres hospitales públicos de la ciudad de Lima. Resultados: El 63% de los participantes fueron del Hospital Nacional Guillermo Almenara Irigoyen, 20% del Hospital San Isidro Labrador, y 17% del Hospital II Vitarte. La prevalencia de la COVID-19 en el personal de enfermería fue de 47.3%. Al analizar las características demográficas de los participantes se evidenció asociación significativa de la variable escolaridad con la enfermedad (OR=1.50, p=0.04), mostrando que los que cuentan con estudios superiores tienen 1.5 veces mayor probabilidad de desarrollar la COVID-19 frente a los que poseen estudios de posgrado; las variables relacionadas con las condiciones de trabajo y factores de riesgo no evidenciaron asociación significativa (p>0.05) con la enfermedad.Conclusiones: A excepción de la escolaridad no existe evidencia suficiente para afirmar asociación significativa de la COVID-19 con las características demográficas, las variables relacionadas al trabajo y las consideradas como riesgo. La investigación aporta contribuciones importantes a la práctica, la investigación y la docencia en enfermería.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.408
Teacher spread0.370 · 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 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

Citations7
Published2022
Admission routes1
Has abstractyes

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