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Record W4386274330 · doi:10.1177/07334648231197074

Changes in Health and Well-Being of Care Aides in Nursing Homes From a Pre-Pandemic Baseline in February 2020 to December 2021

2023· article· en· W4386274330 on OpenAlexafffundabout
Yuting Song, Janice Keefe, Janet E. Squires, Brittany S. DeGraves, Yinfei Duan, Greta G. Cummings, Malcolm Doupe, Matthias Hoben, Amber Duynisveld, Peter Norton, Jeffrey W. Poss, Carole A. Estabrooks

Bibliographic record

VenueJournal of Applied Gerontology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of WaterlooUniversity of CalgaryYork UniversityUniversity of OttawaUniversity of ManitobaMount Saint Vincent UniversityUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsWorkforcePandemicMedicineOddsSample (material)Coronavirus disease 2019 (COVID-19)Nursing homesNursingBaseline (sea)Family medicineGerontologyLogistic regressionDisease

Abstract

fetched live from OpenAlex

Nursing homes were profoundly affected by the COVID-19 pandemic, influencing work outcomes of care aides who provide the most direct care. We compared care aides' quality of work life by conducting a repeated cross-sectional analysis of data collected in February 2020 and December 2021 from a stratified random sample of urban nursing homes in two Canadian provinces. We used two-level random-intercept repeated-measures regression models, adjusting for demographics and nursing home characteristics. 2348 and 1116 care aides completed the survey in February 2020 and December 2021, respectively. The 2021 sample had higher odds of reporting worked short-staffed daily to weekly in the previous month than the 2020 sample. The 2021 sample also had a small but significant drop in professional efficacy and mental health. Despite the worsening changes, our findings suggest that this workforce may have withstood the pandemic better than might be expected.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.384
Teacher spread0.359 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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