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Record W4205120490 · doi:10.1080/09638237.2021.2022623

Proximity to COVID-19 patients and role-specific mental health outcomes of healthcare professionals

2022· article· en· W4205120490 on OpenAlexaff
Yael Mayer, Shir Etgar, Noga Shiffman, Ido Lurie

Bibliographic record

VenueJournal of Mental Health · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMental healthCoronavirus disease 2019 (COVID-19)FeelingStressorAnxietyClinical psychologyMedicineDepression (economics)PsychologyScale (ratio)Social distanceHealth careSocial supportPsychiatryPsychotherapistDiseaseInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Healthcare professionals (HCPs) experience extreme hardships and challenges during the time of COVID-19, due to their professional roles. At the same time, HCPs may experience a feeling of importance as contributing members of the community, which could enhance their well-being alongside COVID-19-work-related stressors. AIMS: This cross-sectional study examined the relationship between HCPs' proximity to COVID-19 patients and role-specific fears of COVID-19 and sense of emotional, social and psychological well-being. METHODS: = 666). Participants completed the Depression and Anxiety Stress Scale-21; Fear of COVID-19 Scale; Fear of COVID-19 Familial Infection Scale; and the Mental Health Continuum Short-Form. RESULTS: Results indicate that the comparison group reported higher levels of fear of COVID-19 compared to secondliners, while frontliners reported the highest levels of fear of infecting their families. Frontliners and secondliners HCPs reported significantly higher levels of social and psychological well-being compared to the non-HCP group. CONCLUSIONS: This study indicates that there are role-specific mental health outcomes related to HCP's proximity to COVID-19 patients.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.463
Teacher spread0.390 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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