OBJECTIVES: To evaluate the psychometric properties of the translated Indonesian version of the Nursing Home Survey on Patient Safety Culture (NHSOPSC-INA).
METHODS: This study was a cross-sectional survey conducted using NHSOPSC-INA. A total of 258 participants from 20 NH in Indonesia were engaged. Participants included NH managers, caregivers, administrative staff, nurses and support staff with at least junior high school education. The SPSS 23.0 was used for descriptive data analysis and internal consistency (Cronbach's alpha) estimation. The AMOS (version 22) was used to perform confirmatory factor analysis (CFA) on the questionnaire's dimensional structure.
RESULTS: The NHSOPSC CFA test originally had 12 dimensions with 42 items and was modified to eight dimensions with 26 items in the Indonesian version. The deleted dimensions were 'Staffing' (4 items), 'Compliance with procedure' (3 items), 'Training and skills' (3 items), 'non-punitive response to mistakes' (4 items) and 'Organisational learning' (2 items). The subsequent analysis revealed an accepted model with 26 NHSOPSC-INA items (root mean square error of approximation = 0.091, comparative fit index = 0.815, Tucker-Lewis index = 0.793, CMIN = 798.488, df = 291, CMIN/Df = 2.74, GFI = 0.782, AGFI = 0.737, p Indonesia. The questionnaire can now be used to evaluate interventions for resident safety in Indonesian NH.
RESULTS: We found evidence of genetic influx from Indians to Malays, more in Melayu Kedah and Melayu Kelantan which are genetically different from the other Malay sub-ethnic groups, but similar to Thai Pattani. More than 98% of these northern Malays haplotypes could be found in either Indians or Chinese populations, indicating a highly admixture pattern among populations. Nevertheless, the ancestry lines of Malays, Indonesians and Thais were traced back to have shared a common ancestor with the Proto-Malays and Chinese.
CONCLUSIONS: These results support genetic admixtures in the Peninsular Malaysia Malay populations and provided valuable information on the enigmatic demographical history as well as shed some insights into the origins of the Malays in the Malay Peninsula.
DESIGN: Data on length/height-for-age percentile values were collected. The LMS method was used for calculating smoothened percentile values. Standardized site effects (SSE) were used for identifying large or unacceptable differences (i.e. $\mid\! \rm SSE \!\mid$ >0·5) between the pooled SEANUTS sample (including all countries) and the remaining pooled SEANUTS samples (including three countries) after weighting sample sizes and excluding one single country each time, as well as with WHO growth references.
SETTING: Malaysia, Thailand, Vietnam and Indonesia.
SUBJECTS: Data from 14202 eligible children were used.
RESULTS: From pair-wise comparisons of percentile values between the pooled SEANUTS sample and the remaining pooled SEANUTS samples, the vast majority of differences were acceptable (i.e. $\mid\! \rm SSE \!\mid$ ≤0·5). In contrast, pair-wise comparisons of percentile values between the pooled SEANUTS sample and WHO revealed large differences.
CONCLUSIONS: The current study calculated length/height percentile values for South East Asian children aged 0·5-12 years and supported the appropriateness of using pooled SEANUTS length/height percentile values for assessing children's growth instead of country-specific ones. Pooled SEANUTS percentile values were found to differ from the WHO growth references and therefore this should be kept in mind when using WHO growth curves to assess length/height in these populations.