RESUMO
AIM: To synthesise evidence from studies that explored the impact of electronic and self-rostering systems to schedule staff on healthcare organisations and healthcare workers. DESIGN: Mixed-method systematic review. METHODS: Studies were screened by two independent reviewers and data were extracted using standardised data extraction tables. The quality of studies was assessed, and parallel-results convergent synthesis was conducted. DATA SOURCES: Academic Search Complete, CINAHL, ERIC, MEDLINE, PsycINFO and PsycARTICLES were searched on January 3, 2023. RESULTS: Eighteen studies were included (10 quantitative descriptive studies, seven non-randomised studies and one qualitative study). Studies examined two rostering interventions including self-rostering (n = 12) and electronic rostering (n = 6). It was found that the implementation of electronic and self-rostering systems for staff scheduling impacted positively on both, healthcare workers and healthcare organisations. Benefits included enhanced roster efficiency, staff satisfaction, greater control and empowerment, improved work-life balance, higher staff retention and reduced turnover, decreased absence rates and enhanced healthcare efficiency. However, self-rostering was found to be less equitable than fixed rostering, was associated with increased overtime, and correlated with a higher frequency of staff requests for shift changes. CONCLUSION: The impact of electronic and self-rostering systems to schedule staff on healthcare organisations and healthcare workers' outcomes was predominantly positive. Further randomised controlled trials and longitudinal studies are warranted to evaluate the long-term impact of various rostering systems, including electronic and self-rostering systems. IMPLICATIONS FOR HEALTHCARE: Rostering is a multifaceted responsibility for healthcare administrators, impacting patient care quality, workforce planning and healthcare expenditure. IMPACT: Given that healthcare staffing costs constitute a substantial portion of global healthcare expenditure, efficient and strategic resource management, inclusive of healthcare staff rostering, is imperative. REPORTING METHOD: The 27-item Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.
Assuntos
Pessoal de Saúde , Humanos , Pessoal de Saúde/psicologia , Admissão e Escalonamento de Pessoal , Satisfação no EmpregoRESUMO
BACKGROUND AND OBJECTIVES: The inability of individuals in the advanced stage of dementia to communicate about preferences in care at the end-of-life poses a challenge for healthcare professionals and family carers. The proven effective Family Carer Decision Support intervention has been designed to inform family carers about end-of-life care options available to a person living with advanced dementia. The objectives of the mySupport study were to adapt the application of the intervention for use in different countries, assess impact on family satisfaction and decision-making, and identify costs and supportive conditions for the implementation of the intervention. RESEARCH DESIGN AND METHODS: A multiple-case study design was chosen where the nursing home was the case. Nursing homes were enrolled from six countries: Canada, Czech Republic, Italy, Netherlands, Republic of Ireland, and United Kingdom. RESULTS: Seventeen cases (nursing homes) participated, with a total of 296 interviews completed including family carers, nursing home staff, and health providers. Five themes relevant to the implementation of the intervention were identified: supportive relationships; committed staff; perceived value of the intervention; the influence of external factors on the nursing home; and resource impact of delivery. DISCUSSION AND IMPLICATIONS: There is a commonality of facilitators and barriers across countries when introducing practice innovation. A key learning point was the importance of implementation being accompanied by committed and supported nursing home leadership. The nursing home context is dynamic and multiple factors influence implementation at different points of time.