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Record W4391647016 · doi:10.1002/nop2.2098

Areas of work‐life, psychological capital and emotional intelligence on compassion fatigue and compassion satisfaction among nurses: A cross‐sectional study

2024· article· en· W4391647016 on OpenAlexaffabout
Stéphanie Maillet, Emily Read

Bibliographic record

VenueNursing Open · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of New BrunswickUniversité de Moncton
Fundersnot available
KeywordsWorkloadCompassion fatigueCompassionPsychologyJob satisfactionBurnoutLife satisfactionNursingClinical psychologySocial psychologyMedicineManagement

Abstract

fetched live from OpenAlex

AIM: To examine the impact of six areas of work-life, emotional intelligence and psychological capital on compassion fatigue and compassion satisfaction among Canadian Registered Nurses and licensed practical nurses. DESIGN: A cross-sectional survey study. METHODS: A convenience sample of 296 Registered Nurses and 110 licensed practical nurses answered a self-administered questionnaire measuring areas of work-life, psychological capital, emotional intelligence, compassion satisfaction and compassion fatigue in September 2019. The association between variables were analysed with descriptive and correlational analyses, while the hypothesized models were tested using multiple regression analyses. RESULTS: This study identified several areas of work-life and intrapersonal resources that impacted compassion satisfaction and compassion fatigue. Among Registered Nurses, compassion satisfaction was predicted by psychological capital, rewards, values and workload. Compassion fatigue was predicted by psychological capital, workload, control and community. Among licensed practical nurses, compassion satisfaction was predicted by psychological capital and emotional intelligence. Compassion fatigue was predicted by workload and psychological capital. Study results also revealed significant differences in Registered Nurses' and licensed practical nurses' perceptions of workload, rewards and fairness at work, and both compassion satisfaction and compassion fatigue. Registered Nurses perceived their workload to be more manageable and perceived greater rewards and greater fairness at work than licensed practical nurses. Compassion fatigue was higher for Registered Nurses than licensed practical nurses, while compassion satisfaction was higher for licensed practical nurses than Registered Nurses. Future studies should investigate the nature and span of these differences to suggest relevant strategies to mitigate compassion fatigue and promote compassion satisfaction for each of these nursing roles. CONCLUSION: The results of this study underscore the need to create nursing work environments that foster a manageable workload and positive social relationships, where nurses have professional autonomy, decision-making capacities and access to adequate resources to do their job effectively. The nursing work environment should also provide recognition of nurses' contributions and an alignment between personal and organizational values. Investments in the development and improvement of nurses' psychological capital and emotional intelligence should be prioritized since they are malleable and impactful intrapersonal resources on compassion satisfaction and compassion fatigue. REPORTING METHOD: This study adhered to the STROBE guidelines. PUBLIC CONTRIBUTION: A total of 406 nurses were involved in this study by answering a self-administered study survey.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.092
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.178
GPT teacher head0.534
Teacher spread0.355 · 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 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

Citations16
Published2024
Admission routes2
Has abstractyes

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