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Record W4387060785 · doi:10.1371/journal.pone.0291988

Worsening of mental health outcomes in nursing home staff during the COVID-19 pandemic in Ireland

2023· article· en· W4387060785 on OpenAlexaff
Conan Brady, Ellie Shackleton, Caoimhe Fenton, Orlaith Loughran, Blánaid Hayes, Martina Hennessy, Agnès Higgins, Iracema Leroi, Deirdre Shanagher, Declan M. McLoughlin

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsTrinity College
Fundersnot available
KeywordsMental healthPandemicMedicineLikert scaleSuicidal ideationNursingScale (ratio)Coping (psychology)BurnoutHealth careFamily medicineCross-sectional studyPsychiatryPsychologySuicide preventionPoison controlClinical psychologyCoronavirus disease 2019 (COVID-19)Medical emergencyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Mental health issues in nursing home staff during the COVID-19 pandemic have been significant; however, it is not known if these issues persist following widespread vaccination and easing of restrictions. OBJECTIVE: To quantify the mental health of nursing home staff at different timepoints during the COVID-19 pandemic in the Republic of Ireland. DESIGN/METHODS: Two identical, online, cross-sectional, nationwide, anonymous surveys of Republic of Ireland nursing home staff at two timepoints (survey 1 (S1, n = 390): November 2020 to January 2021; survey 2 (S2, N = 229: November 2021 to February 2022) during the COVID-19 pandemic. Convenience sampling was used with staff self-selecting for participation. Methods included the World Health Organisation's Well-Being Index (WHO-5), the Impact of Events Scale-Revised (IES-R), the Moral Injury Events Scale (MIES), two Likert-scale items regarding suicidal ideation and planning, the Work Ability Score (WAS), the Brief Coping Orientation to Problems Experienced (Brief-COPE) Scale, and a 15-item questionnaire assessing perceptions of the outbreak with one additional Likert-scale item on altruism. Descriptive analysis examined differences between staff based on their classification in one of three groups: nurses, healthcare assistants (HCA) and nonclinical staff. Pseudonymous identifiers were used to link responses across surveys. RESULTS: An insufficient number of participants completed both surveys for linked analyses to be performed; therefore, we performed an ecological comparison between these two independent surveys. More staff reported moderate-severe post-traumatic stress symptoms (S1 45%; S2 65%), depression (S1: 39%; S2 57%), suicidal ideation (S1: 14%; S2 18%) and suicidal planning (S1: 9%; S2 15%) later in the pandemic. There was a higher degree of moral injury at S2 (S1: 20.8 standard deviation (SD) 9.1; S2: 25.7 SD (11.3)) and use of avoidant (maladaptive) coping styles at S2 (S1: 20.8 (6.3); S2 23.0 (6.3)) with no notable differences found in the use of approach (adaptive) coping styles. Staff reported more concerns at S2 regarding contracting COVID-19, social stigma, job stress, doubts about personal protective equipment and systems and processes. CONCLUSION: In comparison to our previous survey, mental health outcomes appear to have worsened, coping did not improve, and staff concerns, and worries appear to have increased as the pandemic progressed. Follow-up studies could help to clarify is there are any lingering problems and to assess if these issues are related to the pandemic and working conditions in nursing homes.

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 score0.048
Threshold uncertainty score0.095

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.143
GPT teacher head0.432
Teacher spread0.289 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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