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Record W3104063986 · doi:10.5430/jha.v9n5p31

Should health care institutions provide job accommodations for health care workers with serious mental health concerns during the COVID-19 pandemic?

2020· article· en· W3104063986 on OpenAlexvenueno aff
Alexandra M. Villagran, Janet Malek, Sophie C. Schneider, Christi J. Guerrini

Bibliographic record

VenueJournal of Hospital Administration · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPandemicHarmHealth careTollMedicineVulnerability (computing)DutyNursingOccupational safety and healthEnvironmental healthCoronavirus disease 2019 (COVID-19)PsychologyDiseasePsychiatryInfectious disease (medical specialty)Political scienceSocial psychology

Abstract

fetched live from OpenAlex

As the Coronavirus Disease 2019 pandemic continues, increased attention has been given to its mental health impacts on frontline health care workers. There is a consensus, consistent with established standards applicable to the duty to treat, that health care workers who are especially vulnerable to risk of physical harm should be provided job accommodations to reduce their risk of disease exposure, but it is unclear whether health care workers should be provided similar accommodations if their vulnerability relates specifically to mental health concerns. Especially given emerging evidence that the pandemic is taking a heavy toll on the mental health of health care workers, this issue should be included in policy conversations involving support of health care workers and provision of resources to them during the pandemic. Arguments in favor of expanding accommodations to those with mental health concerns include institutions’ ethical duty to protect vulnerable workers and not discriminate against their employees, as well as broader consideration of the consequences of not providing accommodations, both for health care workers and 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.630
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.133
GPT teacher head0.481
Teacher spread0.348 · 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 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
Published2020
Admission routes1
Has abstractyes

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