PURPOSE
This study is to determine the predictive validity of the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) for inpatients' fall risk.
METHODS
A literature search was performed to identify all studies published between 1946 and 2014 from periodicals indexed in Ovid Medline, Embase, CINAHL, KoreaMed, NDSL and other databases, using the following key words; 'fall', 'fall risk assessment', 'fall screening', 'mobility scale', and 'risk assessment tool'. The QUADAS-II was applied to assess the internal validity of the diagnostic studies. Fourteen studies were analyzed using meta-analysis with MetaDisc 1.4.
RESULTS
The predictive validity of STRATIFY was as follows; pooled sensitivity .75 (95% CI: 0.72~0.78), pooled specificity .69 (95% CI: 0.69~0.70) respectively. In addition, the pooled sensitivity in the study that targets only the over 65 years of age was .89 (95% CI: 0.85~0.93).
CONCLUSION
The STRATIFY's predictive validity for fall risk is at a moderate level. Although there is a limit to interpret the results for heterogeneity between the literature, STRATIFY is an appropriate tool to apply to hospitalized patients of the elderly at a potential risk of accidental fall in a hospital.

Citations

• Development of a fall prediction model for community-dwelling older adults in South Korea using machine learning: a secondary data analysis
  Minhee Suh, Hyesil Jung, Juli Kim
  Journal of Korean Biological Nursing Science.2024; 26(4): 288.     CrossRef
• Trends of Nursing Research on Accidental Falls: A Topic Modeling Analysis
  Yeji Seo, Kyunghee Kim, Ji-Su Kim
  International Journal of Environmental Research and Public Health.2021; 18(8): 3963.     CrossRef
• Systematic Review and Meta-analysis for Usefulness of Fall Risk Assessment Tools in Adult Inpatients
  Seong-Hi Park, Eun-Kyung Kim
  Korean Journal of Health Promotion.2016; 16(3): 180.     CrossRef

PURPOSE
The aim of this study was to investigate the accuracy of infrared temperature measurements compared to axillary temperature in order to detect fever in patients.
METHODS
Studies published between 1946 and 2012 from periodicals indexed in Ovid Medline, Embase, CINAHL, Cochrane, KoreaMed, NDSL, KERIS and other databases were selected using the following key words: "infrared thermometer." QUADAS-II was utilized to assess the internal validity of the diagnostic studies. Selected studies were analyzed through a meta-analysis using MetaDisc 1.4.
RESULTS
Twenty-one diagnostic studies with high methodological quality were included representing 3,623 subjects in total. Results of the meta-analysis showed that the pooled sensitivity, specificity, and area under the curve (AUC) of infrared tympanic thermometers were 0.73 (95% CI 0.70~0.75), 0.92 (95% CI 0.91~0.92), and 0.90, respectively. For axillary temperature readings, the pooled sensitivity was 0.67 (95% CI 0.62~0.73), the pooled specificity was 0.87 (95% CI 0.85~0.90), and the AUC was 0.80.
CONCLUSION
Infrared tympanic temperature can predict axillary temperature in normothermic and in febrile patients with an acceptable level of diagnostic accuracy. However, further research is necessary to substantiate this finding in patients with hyperthermia.

Citations

• Development of heat stress index for healthcare workers with personal protective equipment
  Yudong Mao, Yongcheng Zhu, Xiwen Feng, Zhaosong Fang
  Energy and Buildings.2025; 346: 116150.     CrossRef

PURPOSE
The present study was conducted in order to examine claustrophobia, noise sensitivity and vital signs according to anxiety sensitivity level in patients who have Magnet Resonance Imaging(MRI).
METHODS
With 100 outpatients, we measured anxiety sensitivity, claustrophobia, noise sensitivity and vital sign before and after MRI. Measuring tools were ASI, CLQ-M, and NSI. Data were collected from February to March, 2008.
RESULTS
The ASI score was higher in women than in men(p < .05), and no statistically significant difference was observed according to age, region of scanning, experience in MRI, and the use of contrast agent. Both men and women patients showed the same ASI score and decrease in CLQ-M and NSI between before and after MRI. In women, ASI, CLQ-M and NSI were in positive correlation with one another(p < .001), and in men, there was no correlation between ASI and CLQ-M, and positive correlation was observed with NSI(p < .05). In comparison according to ASI level, blood pressure and pulse rate were not different in men and women. CLQ-M was not different in men, but was different in women(p < .001). NSI was different in both men and women(men p < .05; women p < .001).
CONCLUSION
MRI may cause claustrophobia in patients with high anxiety sensitivity, and noise appears to aggravate anxiety. In particular, claustrophobia was more serious in women than in men. Therefore, it is necessary to develop nursing interventions to reduce anxiety sensitivity particularly for female patients, and to make plans to educate and lower noise before MRI in order to reduce claustrophobia.

PURPOSE
The aims of this study were to develope a structural model of health insensitivity and to verify the model of health insensitivity. METHOD: There were three theoretical variables in the hypothetical model. The endogenous variable was health insensitivity which is a concept including bluntness of health risk perception and unhealthy behavior. The exogenous variables were composed of personal factors and socio-cultural factors. In personal factors, neuroticism, external health locus of control, blunting style of information-seeking, deficit of self-efficacy, knowledge deficit related to health, health-related experience, age and education were included. Whereas socio-cultural factors include perceived group size of unhealthy behavior and stereotypes of unhealthy behavior. RESULT: Personal factors and sociocultural factors were significant in explanation of the health insensitivity. Relationship between personal factors and sociocultural factors was significant, too. However, the optimistic bias as part of health insensitivity was not supported by these data. GFI, AGFI and PGFI were .95, .92, .65, respectively. Therefore, this model was verified to be a good fit to the data and parsimonious. CONCLUSION: Nursing to change unhealthy behavior has focused on personal factors rather than sociocultural factors. Based on this result, however, the sociocultural factors should be considered as well.