Aim: This paper is a report of a study of the relations of coaching and developing clinical practice on nurses' work place attitudes and self-reported performance, as mediated by role breadth self-efficacy and flexible role orientation.
Background: Previous research into the effectiveness of nurses' learning and development activities has mainly focused on specific skill and knowledge acquisition outcomes. Few studies investigate the relationship between learning and development activities and work attitudes or performance, or explore mediating mechanisms in this process. Previous literature suggests that malleable cognitive and motivational constructs may be important mechanisms for improving work attitudes and proactive performance.
Method: We surveyed 404 qualified nurses from a large, metropolitan public hospital in Australia in 2006 using validated measures from previous research. Descriptive statistics, correlation analysis and hierarchical regression analyses were conducted.
Results: The results show a clear association between learning and development activities and work attitudes and performance. Developing clinical practice improved self-rated performance and coaching improved work attitudes. In addition, role breadth self-efficacy and flexible role orientation mediated these relationships and emerge as important mechanisms in the link between learning and development and work attitudes and performance.
Conclusion: Investment in learning and development activities for nurses improves outcomes for nurses, the organization and patients.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/j.1365-2648.2010.05487.x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A blended learning approach for capacity strengthening to improve the quality of integrated HIV, TB, and malaria services during antenatal and postnatal care in LMICs: a feasibility study.
BMC Med Educ
January 2025
Department of International Public Health, Emergency Obstetric and Quality of Care Unit, Liverpool School of Tropical Medicine, Pembrooke Place, L3, 5QA, Liverpool, UK.
Background: The blended learning (BL) approach to training health care professionals is increasingly adopted in many countries because of high costs and disruption to service delivery in the light of severe human resource shortage in low resource settings. The Covid-19 pandemic increased the urgency to identify alternatives to traditional face-to-face (f2f) education approach. A four-day f2f antenatal care (ANC) and postnatal care (PNC) continuous professional development course (CPD) was repackaged into a 3-part BL course; (1) self-directed learning (16 h) (2) facilitated virtual sessions (2.
View Article and Find Full Text PDFDeveloping clinical reasoning along the cognitive continuum: a mixed methods evaluation of a novel Clinical Diagnosis Assessment.
BMC Med Educ
January 2025
Deakin Optometry, School of Medicine, Deakin University, 75 Pigdons Road, Waurn Ponds , VIC, 3216, Australia.
Background: Clinical reasoning is a professional capability required for clinical practice. In preclinical training, clinical reasoning is often taught implicitly, and feedback is focused on discrete outcomes of decision-making. This makes it challenging to provide meaningful feedback on the often-hidden metacognitive process of reasoning to address specific clinical reasoning difficulties.
View Article and Find Full Text PDFA machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes.
BMC Med Genomics
January 2025
Administrative Office, The Fourth People's Hospital of Nanning, Nanning, China.
Background: Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease.
View Article and Find Full Text PDFEarly childhood caries risk prediction using machine learning approaches in Bangladesh.
BMC Oral Health
January 2025
Department of Clinical Dentistry, Faculty of Medicine, University of Bergen, Bergen, Norway.
Background: In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifying crucial health behaviours within mother-child pairs.
Methods: For the analysis, we utilized a representative sample of 724 mothers with children under six years in Bangladesh.
Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review.
BMC Med Inform Decis Mak
January 2025
Department of Digital Systems, University of Piraeus, Piraeus, Greece.
Vitiligo, alopecia areata, atopic, and stasis dermatitis are common skin conditions that pose diagnostic and assessment challenges. Skin image analysis is a promising noninvasive approach for objective and automated detection as well as quantitative assessment of skin diseases. This review provides a systematic literature search regarding the analysis of computer vision techniques applied to these benign skin conditions, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!