Literature DB >> 27237454

Understanding well-being and learning of Nigerian nurses: a job demand control support model approach.

Yvonne van Doorn1, Joris van Ruysseveldt2, Karen van Dam1, Wilhelm Mistiaen3, Irina Nikolova4.   

Abstract

AIM: This study investigated whether Nigerian nurses' emotional exhaustion and active learning were predicted by job demands, control and social support.
BACKGROUND: Limited research has been conducted concerning nurses' work stress in developing countries, such as Nigeria. Accordingly, it is not clear whether work interventions for improving nurses' well-being in these countries can be based on work stress models that are developed in Western countries, such as the job demand control support model, as well as on empirical findings of job demand control support research.
METHOD: Nurses from Nurses Across the Borders Nigeria were invited to complete an online questionnaire containing validated scales; 210 questionnaires were fully completed and analysed. Multiple regression analysis was used to test the hypotheses.
RESULTS: Emotional exhaustion was higher for nurses who experienced high demands and low supervisor support. Active learning occurred when nurses worked under conditions of high control and high supervisor support.
CONCLUSION: The findings suggest that the job demand control support model is applicable in a Nigerian nursing situation; the model indicates which occupational stressors contribute to poor well-being in Nigerian nurses and which work characteristics may boost nurses' active learning. IMPLICATIONS FOR NURSING MANAGEMENT: Job (re)design interventions can enhance nurses' well-being and learning by guarding nurses' job demands, and stimulating job control and supervisor support.
© 2016 John Wiley & Sons Ltd.

Keywords:  Nigeria; active learning; emotional exhaustion; job demand control support-model; nurses

Mesh:

Year:  2016        PMID: 27237454     DOI: 10.1111/jonm.12397

Source DB:  PubMed          Journal:  J Nurs Manag        ISSN: 0966-0429            Impact factor:   3.325


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