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Record W3024770785

An online survey of factors associated with self-perceived stress during the initial stage of the COVID-19 outbreak in Nepal

2020· article· en· W3024770785 on OpenAlexaboutno aff
Saurav Chandra Acharya Samadarshi, Sharmistha Sharma, Jeevan Bhatta

Bibliographic record

VenueEthiopian Journal of Health Development · 2020
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsOutbreakCoronavirus disease 2019 (COVID-19)Perceived Stress ScaleAffect (linguistics)PandemicIntervention (counseling)PsychologyQuarter (Canadian coin)Stress (linguistics)ChinaMedicineDiseaseEnvironmental healthScale (ratio)GerontologyGeographyPsychiatryInfectious disease (medical specialty)VirologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Background: The novel coronavirus (COVID-19) is global challenge humankind has ever witnessed in recent times. After its outbreak in late December in Wuhan China, it has expanded to affect the entire world. In as much as it is a new disease, there is dearth of evidence. Aim: The aim of this study is to find and assess the factors associated with self-perceived stress during the COVID-19 outbreak in Nepal. Method: We evaluated 374 respondents from an online survey, using the Sheldon Cohen Perceived Stress Scale, to assess stress levels during the COVID-19 outbreak in Nepal. Results: Nearly three-quarters of the respondents rated their self-perceived stress as moderate to high, and about one quarter reported to have low self-perceived stress. Age and employment status were associated with a greater psychological impact of the outbreak. Conclusions: There is a need to carry out psychological intervention activities through various mediums to help people become more resilient during the COVID-19 epidemic. [Ethiop. J. Health Dev. 2020; 34(2):84-89] Keywords: COVID-19, psychological response, stress, Nepal

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)0.003
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 score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.257
GPT teacher head0.442
Teacher spread0.186 · 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

Citations39
Published2020
Admission routes1
Has abstractyes

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