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Record W4321996765 · doi:10.1002/nop2.1678

Report on fraying resilience among the Ontario Registered Practical Nurse Workforce in long‐term care homes during <scp>COVID</scp>‐19

2023· article· en· W4321996765 on OpenAlexafffundabout
Denise M. Connelly, Nancy Snobelen, Anna Garnett, Nicole A. Guitar, Cecilia Flores‐Sandoval, Samir K. Sinha, Jen Calver, Diana Pearson, Tracy Smith‐Carrier

Bibliographic record

VenueNursing Open · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsLambton CollegeRoyal Roads UniversityUniversity of TorontoSumitomo Precision Products (Canada)Western University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsResilience (materials science)WorkforceScale (ratio)Long-term careWork (physics)NursingCoronavirus disease 2019 (COVID-19)Psychological resiliencePersonal protective equipmentBurnoutPsychologyMedicineGeography

Abstract

fetched live from OpenAlex

AIM: Registered Practical Nurses (RPNs) are frontline healthcare providers in Ontario long-term care (LTC) homes. Throughout COVID-19, RPNs working in LTC homes experienced prolonged lockdowns, challenging working conditions, and inadequate resource allocation. This study aimed to describe the personal and professional resilience of RPNs working in LTC during the COVID-19 pandemic. DESIGN: An open cross-sectional online survey containing the Connor-Davidson Resilience Scale, Resilience at Work Scale®, and Resilience at Work Team Scale®. METHODS: The survey was distributed by the RPN Association of Ontario (WeRPN) to approximately 5000 registered members working in Ontario LTC homes. RESULTS: A total of 434 respondents participated in the survey (completion rate = 88.0%). Study respondents scored low on measures of resilience and reported extreme levels of job (54.5%) and personal (37.8%) stress. Resources to support self-care and work-life balance, build capacity for team-based care practice(s) are needed.

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 categoriesMeta-epidemiology (narrow), Science 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 score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.092
GPT teacher head0.454
Teacher spread0.362 · 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

Citations13
Published2023
Admission routes3
Has abstractyes

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