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Record W3120156899 · doi:10.1177/0950017020977314

Residential Care Aides’ Experiences of Workplace Incivility in Long-Term Care

2021· article· en· W3120156899 on OpenAlexafffundabout
Heather A. Cooke, Jennifer Baumbusch

Bibliographic record

VenueWork Employment and Society · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of British Columbia
FundersMichael Smith Health Research BCWorkSafeBCAlzheimer Society
KeywordsIncivilityWorkforceEthnographyResidential careWorkplace bullyingNursingPsychologyPeer mentoringCare workWork (physics)MedicineSocial psychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Exposure to peer incivility and bullying potentially disrupts the respectful, collaborative workplace relationships essential to quality care provision in long-term care homes. This study critically examined the nature of peer incivility and bullying in residential care aides’ workplace relationships. Using critical ethnography, 100 hours of participant observation and 33 semi-structured interviews were conducted with residential care aides, licensed practical nurses, support staff and management in two, non-profit care homes in British Columbia, Canada. While residential care aides’ experiences of bullying were rare, peer incivility was pervasive, occurring on an almost daily basis. Two key themes, ‘gendered work environment’ and ‘seeking informal power and control’, reflect how residential care aides experienced and explained their uncivil encounters. Findings highlight the gendered, relationally aggressive nature of workplace mistreatment within this predominantly female workforce.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.302
Teacher spread0.287 · 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 designQualitative
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
Published2021
Admission routes3
Has abstractyes

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