Development of the 'SNS older adults measure' (SNS-OA) to examine social network site use in older adults.

OBJECTIVES: Social Networking Sites (SNSs) may ameliorate loneliness in later life but no measure of SNS use for this population exists. This study describes the development of the 'SNS Older Adults measure' (SNS-OA), to improve understanding of older adults' SNS use and its relationship to social wellbeing. METHODS: The SNS-OA underwent initial development, including literature reviews and consultation with target population (n = 9) and experts (n = 9); piloting (n = 74), and evaluation of psychometric properties (n = 263). RESULTS: The final measure comprised three 'motive' scales (using SNSs to maintain close ties, maintain and strengthen weaker ties and diversion), and two 'affect' scales (positive/negative). Whilst many items were weakly endorsed by participants, the measure demonstrated good reliability (Cronbach α = 0.85; ICC = 0.82) and some convergent validity, with some subscales correlating with a personality measure in hypothesised directions. No statistically significant correlations were observed between the measure and social wellbeing. CONCLUSIONS: Despite the measure's limitations, this research has enabled a better understanding of SNS use in older adults and has important implications for research in this area. Findings also suggest a complex relationship between social wellbeing and SNS use in later life.

Factors Associated with Length of Stay in Psychiatric Inpatient Services in London, UK.

The purpose of this research was to identify factors associated with lengthy stays in psychiatric hospital in a UK mental health trust. A multiple regression using a multiple imputation method to deal with missing data was performed on inpatient admissions data over a four-year period for 7653 individuals. Factors associated with a longer length of stay included gender (being male), ethnicity (being Asian/Asian British, Black/Black British, or having a mixed background compared to being White/White British), accommodation status (being homeless, or in accommodation with support), primary diagnostic group of psychosis (F20-29), and number of care coordinators. Marital status was not found to be associated with length of stay in this sample. Length of stay is likely to be multifactorially determined, and more similar studies examining factors associated with length of hospital stay are needed to understand the operation of psychiatric services.

Rapid and frequent psychiatric readmissions: associated factors.

OBJECTIVE: This article aims to examine the impact of clinical and demographic factors on both rapid readmission (being readmitted within 30 d) and frequent readmission (experiencing three or more admissions) in a London mental health trust. METHODS: A four-year study period with a sample of 7648 inpatients were examined with chi-square or Mann-Whitney U tests, and binary logistic regressions to assess the associations and possible predictors of rapid (readmitted within 30 d) and frequent (admitted at least three times) readmission. RESULTS: Frequent admitters were more likely to be unemployed, have more care-coordinators, be living in accommodation with support, be Black or Black British, and to have certain diagnoses. Binary logistic regression revealed that our variables of interest explained between 36.1 and 60.9% of the variation in number of readmissions (±3 admissions), and between 3.7 and 4.2% of the variation in time to readmission (±30 d). CONCLUSIONS: Identifying factors that are associated with rehospitalisation and understanding their importance is necessary to reduce the risk of readmission. This study suggests that particular demographic, clinical and treatment factors require consideration to tackle the seemingly wide range of factors that could be affecting readmission to inpatient services.

Statistical approaches for identifying heavy users of inpatient mental health services.

BACKGROUND: A lack of consensus exists concerning how to identify "heavy users" of inpatient mental health services. AIM: To identify a statistical approach that captures, in a clinically meaningful way, "heavy" users of inpatient services using number of admissions and total time spent in hospital. METHODS: "Simple" statistical methods (e.g. top 2%) and data driven methods (e.g. the Poisson mixture distribution) were applied to admissions made to adult acute services of a London mental health trust. RESULTS: The Poisson mixture distribution distinguished "frequent users" of inpatient services, defined as having 4 + admissions in the study period. It also distinguished "high users" of inpatient services, defined as having 52 + occupied bed days. Together "frequent" and "high" users were classified as "heavy users". CONCLUSIONS: Data driven criteria such as the Poisson mixture distribution can identify "heavy" users of inpatient services. The needs of this group require particular attention.