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Record W4408782397 · doi:10.1177/07334648251328400

Care Aides Compassion Fatigue, Burnout, and Compassion Satisfaction Related to Long-Term Care (LTC) Working Environment

2025· article· en· W4408782397 on OpenAlexafffundabout
Ashikur Rahman, Yinfei Duan, Holly Symonds‐Brown, Jordana Salma, Carole A. Estabrooks

Bibliographic record

VenueJournal of Applied Gerontology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsBurnoutCompassion fatigueStaffingEmotional exhaustionPsychologyCompassionJob satisfactionLong-term careNursingMedicineClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

Severe staff shortages, sustained stress, low compassion satisfaction, high compassion fatigue, and serious levels of burnout among healthcare workers were frequently reported during COVID-19. In this cross-sectional study with 760 care aides working in 28 LTC homes in Alberta, Canada, we used a two-level multilevel regression model to examine how working environments were associated with compassion fatigue, burnout, and compassion satisfaction measured with the Professional Quality of Life (ProQOL-9) scale. Our findings showed that higher compassion satisfaction and lower burnout were observed when care aides perceived a more supportive working culture. Care aides reported higher compassion fatigue when there was a lack of structural or staffing resources. We also found that perceptions of not having enough staff or enough time to complete tasks were significantly associated with higher levels of burnout. These findings suggest which elements of the working environment may be promising targets for improvement efforts.

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 score0.379
Threshold uncertainty score0.987

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.049
GPT teacher head0.406
Teacher spread0.357 · 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

Citations5
Published2025
Admission routes3
Has abstractyes

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