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Record W4322616885 · doi:10.3928/00989134-20230209-03

Residential Aged Care Facilities During the COVID-19 Pandemic: A Staff Survey on Impact and Resources

2023· article· en· W4322616885 on OpenAlexaboutno aff
Danielle Ní Chróinín, Allicia Anthony, Renee Acosta, Deborah Thambyaiyah, Nasrin Hasan, Arvin Patil

Bibliographic record

VenueJournal of Gerontological Nursing · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingCoronavirus disease 2019 (COVID-19)PandemicMedicineLong-term careOutreachQuarter (Canadian coin)Aged careNursingFamily medicineDisease

Abstract

fetched live from OpenAlex

The current study explored the impact of the coronavirus disease 2019 (COVID-19) pandemic on staff in residential aged care facilities (RACFs). A hardcopy, voluntary, anonymous survey was circulated to local RACFs (June–July 2020), exploring challenges, staffing effects, mood within RACFs, and staff perceptions of supports. Overall, 105 staff members responded, which were mainly nursing personnel (67.6%) and owners/managers (10.5%). Seventy percent believed they were equipped to handle patients with COVID-19. One quarter reported personal protective equipment shortages. Respondents reported pressures to accept patients with COVID-19 from hospitals and/or keep residents in the RACF. One third reported staff “calling in sick” related to COVID-19/quarantine. Common compensatory strategies included increasing part-time workers' hours. Reported mood was largely positive. Most (86.4%) respondents felt supported by general practitioner and local geriatric outreach services. Opportunities to best support RACF staff require further research and dialogue. [ Journal of Gerontological Nursing, 49 (3), 13–17.]

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.161
GPT teacher head0.461
Teacher spread0.300 · 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
Published2023
Admission routes1
Has abstractyes

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