Development and psychometric evaluation of the hidden curriculum assessment scale in nursing education: A validity and reliability study.

AIMS: The study aimed to develop and psychometrically evaluate a measurement scale for identifying and assessing the hidden curriculum in undergraduate nursing education. BACKGROUND: The hidden curriculum is a general term for educational information that exists outside of the teaching program and mainly affects students' knowledge, emotions, behaviors, beliefs, values and professional ethics. However, a specific instrument to comprehensively define and assess the hidden curriculum in nursing education has not yet been developed in China. DESIGN: A descriptive and explorative study design was used. METHODS: We developed the initial scale through a literature review, focus group discussion, Delphi expert consultation and pre-survey. From February to April 2023, the data were collected from a convenient sample of 512 nursing students enrolled in five medical universities in China to conduct exploratory factor analysis and confirmatory factor analysis for validity testing. In addition, reliability analysis was conducted by calculating Cronbach's alpha coefficients, split-half reliability and test-retest reliability. The nursing students' responses were evaluated using a five-point Likert scale. RESULTS: The Hidden Curriculum Assessment Scale in Nursing Education (HCAS-NE) was formulated, consisting of 4 dimensions and 35 items. Exploratory factor analysis extracted four factors, with a cumulative variance contribution rate of 66.863 % and confirmatory factor analysis indicated that the fit indices values of the scale structure model met the criteria for an ideal level. the Cronbach's α coefficient of the scale was 0.965, the Guttman split-half was 0.853 and the test-retest reliability was 0.967. CONCLUSION: This study demonstrated that the Hidden Curriculum Assessment Scale in Nursing Education (HCAS-NE) has ideal reliability and validity, which provides a valid and reliable tool for identifying and assessing the hidden curriculum in nursing education.

Real-time semi-quantitative assessment of anastomotic blood perfusion in mini­invasive rectal resections by Sidestream Dark Field (SDF) imaging technology: a prospective in vivo pilot study.

PURPOSE: Anastomotic leakage (AL) is one of the severe complications after rectal surgery, and anastomotic ischemia is one of the main factors. This prospective in vivo pilot study aimed to evaluate the effectiveness of Sidestream Dark Field (SDF) imaging in quantitative assessment of anastomotic microcirculation and to analyze its correlation with AL. METHODS: Thirty-three patients with rectal cancer who underwent laparoscopic low anterior resection from 2019 to 2020 were enrolled. Microcirculation was measured by SDF imaging at the descending colon, the mesocolon transection line (MTL), and 1 cm and 2 cm distal to the MTL. Anastomotic microcirculation was measured at the stapler anvil edge before anastomosis. Quantitative perfusion-related parameters were as follows: microcirculation flow index (MFI), perfused vessel density (PVD), proportion of perfused vessels (PPV), and total vessel density (TVD). RESULTS: All patients obtained stable microcirculation images. Functional microcirculation parameters (MFI, PPV, PVD) decreased successively from the descending colon, the colon at MTL, and 1 cm and 2 cm distal to the MTL (all P < 0.01). Extremely poor microcirculation was found at the intestinal segment 2 cm distal to the MTL. Micro-perfusion was significantly lower at the colonic limb of the anastomosis compared with the descending colon (all P < 0.001). Anastomotic leakage occurred in 3 patients (9.1%) whose anastomotic microcirculation was significantly lower than those without AL (all P < 0.01). Blood perfusion at the colonic limb of the anastomosis was significantly higher in patients with left colic artery preservation than in controls. CONCLUSION: SDF imaging is a promising technique for evaluating anastomotic microcirculation and has potential clinical significance for risk stratification of AL.

Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.

The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.