METHODS: We developed the International Diet-Health Index (IDHI) to measure health impacts of dietary intake across 186 countries in 2010, using age-specific and sex-specific data on country-level dietary intake, effects of dietary factors on cardiometabolic diseases and country-specific cardiometabolic disease profiles. The index encompasses the impact of 11 foods/nutrients on 12 cardiometabolic diseases, the mediation of health effects of specific dietary intakes through blood pressure and body mass index and background disease prevalence in each country-age-sex group. We decomposed the index into IDHIbeneficial for risk-reducing factors, and IDHIadverse for risk-increasing factors. The flexible functional form of the IDHI allows inclusion of additional risk factors and diseases as data become available.
RESULTS: By sex, women experienced smaller detrimental cardiometabolic effects of diet than men: (females IDHIadverse range: -0.480 (5th percentile, 95th percentile: -0.932, -0.300) to -0.314 (-0.543, -0.213); males IDHIadverse range: (-0.617 (-1.054, -0.384) to -0.346 (-0.624, -0.222)). By age, middle-aged adults had highest IDHIbeneficial (females: 0.392 (0.235, 0.763); males: 0.415 (0.243, 0.949)) and younger adults had most extreme IDHIadverse (females: -0.480 (-0.932, -0.300); males: -0.617 (-1.054, -0.384)). Regionally, Central Latin America had the lowest IDHIoverall (-0.466 (-0.892, -0.159)), while Southeast Asia had the highest IDHIoverall (0.272 (-0.224, 0.903)). IDHIoverall was highest in low-income countries and lowest in upper middle-income countries (-0.039 (-0.317, 0.227) and -0.146 (-0.605, 0.303), respectively). Among 186 countries, Honduras had lowest IDHIoverall (-0.721 (-0.916, -0.207)), while Malaysia had highest IDHIoverall (0.904 (0.435, 1.190)).
CONCLUSION: IDHI encompasses dietary intakes, health effects and country disease profiles into a single index, allowing policymakers a useful means of assessing/comparing health impacts of diet quality between populations.
METHODS: 142 new nurses were chosen for the investigation using a convenient cluster sampling method. The questionnaire included components on socio-demographic characteristics, the Competency Inventory for Registered Nurses (CIRN), and the PsyCap Questionnaire-24 (PCQ-24). The t-test, One-Way ANOVA, Pearson correlation analysis and hierarchical multiple regression were used for statistical analysis.
RESULT: The number of valid questionnaires was 138, and the effective return rate was 97.2%. The overall mean score for core competencies was 171.01 (SD 25.34), and the PsyCap score was 104.76(SD 13.71). The PsyCap of new nurses was highly correlated with core competency, with a correlation coefficient of r = 0.7, p < 0.01. Self-efficacy of PsyCap is a significant independent predictor of core competency (adjust R2 = 0.49).
CONCLUSION: Self-efficacy in PsyCap is an important predictor of new nurses' core competency. Nursing managers should pay sufficient attention to the cultivation and development of new nurses' PsyCap, with particular emphasis on enhancing self-efficacy to improve their core competency.