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Record W3115843869 · doi:10.1177/0733464820980567

Factors Influencing Nurse Assistants’ Job Satisfaction in Nursing Homes in Canada and Spain: A Comparison of Two Cross-Sectional Observational Studies

2020· article· en· W3115843869 on OpenAlexaffabout
Katherine S. McGilton, Steven Stewart, Jennifer Bethell, Charlene H. Chu, José Tomás Mateos, Roland Pastells‐Peiró, Joan Blanco‐Blanco, Míriam Rodríguez‐Monforte, Astrid Escrig-Piñol, Montserrat Gea‐Sánchez

Bibliographic record

VenueJournal of Applied Gerontology · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsPublic Health OntarioToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsJob satisfactionAssociation (psychology)Observational studyNursingPsychologyCross-sectional studyMultilevel modelMedicineSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: To access associations between job satisfaction and supervisory support as moderated by stress. METHODS: For this cross-sectional study, data collected from 591 nursing assistants in 42 nursing homes in Canada and Spain were analyzed with mixed-effects regression. RESULTS: In both countries, stress related to residents' behaviors was negatively associated with job satisfaction, and, in Canada, it moderated the positive association between supervisory support and job satisfaction. Stress related to family conflict issues moderated the positive association of supervisory support and job satisfaction differently in each location: in Canada, greater stress was associated with a weaker association between supervisory support and job satisfaction; in Spain, this was also observed but only when supervisory support was sufficiently weak. DISCUSSION: Stress was associated with lower job satisfaction and moderated the association of supervisory support and job satisfaction, reinforcing the importance of supervisors supporting nursing assistants, especially during the COVID-19 pandemic.

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)0.000
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 score0.605
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.195
GPT teacher head0.461
Teacher spread0.266 · 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

Citations9
Published2020
Admission routes2
Has abstractyes

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