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Validation of the CONSULT-PSYCHIATR Scale: reasons for reluctance to seek psychiatric consultation among workers in Peru
Introduction: Workers may be reluctant to attend psychiatric consultation because of stigma, interpersonal concerns, and practical barriers. In Peru, brief instruments specifically designed to assess these reasons in working populations are limited. This study examined the content validity, internal structure, and internal consistency of a Peruvian scale assessing reasons why workers are reluctant to attend consultations with a psychiatrist.Methods: An instrumental study was conducted in Peru. Item development was informed by a literature review and expert input. An initial 10-item version was evaluated by 20 experts for relevance, representativeness, and clarity using Aiken’s V. After a pilot test, the questionnaire was administered to 3001 workers from several Peruvian departments using non-random sampling. Item distribution was examined using descriptive statistics, skewness, excess kurtosis, floor and ceiling effects, and constant-response patterns. The sample was randomly divided into exploratory and confirmatory subsamples. A polychoric exploratory factor analysis was conducted in the exploratory subsample, followed by confirmatory analyses in the independent confirmatory subsample. Internal consistency was estimated using Cronbach’s alpha, ordinal alpha, and ordinal omega total.Results: Most Aiken’s V coefficients were 0.70 or higher, with only one lower value for the representativeness of item 9. The 10-item version showed adequate item distributions, although 46.95% of participants provided constant responses across all items, prompting sensitivity analyses. Exploratory factor analysis supported a one-factor structure, explaining 80.96% of the variance, with factor loadings ranging from 0.8425 to 0.9365. This structure remained stable after excluding constant-response patterns. In the confirmatory subsample, the 10-item one-factor model showed high standardized loadings and favorable CFI, TLI, and SRMR values, although RMSEA was less favorable and was interpreted jointly with the remaining indices. Reliability estimates were high in both the main and sensitivity analyses.Discussion: The CONSULT-PSYCHIATR is a brief 10-item instrument with evidence of content validity, internal structure, and internal consistency for assessing perceived reasons why workers in Peru may be reluctant to attend psychiatric consultation. Although the findings are encouraging, further studies are needed to examine its performance in other populations and settings and to assess additional sources of validity evidence.
Development of the Semi-Implicit Self-Report to assess Attitudes Toward Immigration (SISRATI): Psychometric properties and convergence
Attitudes toward immigration in Spain represent a pressing social concern, particularly in the context of increasing reports of racism and public rejection and concern. However, assessing these attitudes is complicated by social desirability bias inherent in self-report measures. This study aimed to develop and validate a semi-implicit instrument using news headlines—half pro-immigration and half anti-immigration—which participants (N = 616, divided into two sub-samples) judged as true or false. Psychometric properties were evaluated through Exploratory and Confirmatory Factor Analyses, revealing a two-factor structure comprising 12 items: “Pro-Immigration Statements” and “Anti-Immigration Statements.” Based on these dimensions, participants were clustered into four groups: “Favorable to Immigration,” “Unfavorable to Immigration,” “Non-Discriminant Believers,” and “Non-Discriminant Unbelievers.” These groups were then compared on emotional responses (valence and intensity) to immigration-related content, Big Five personality traits, and reflective cognitive style. Significant differences in emotional responses across groups supported the tool’s convergent validity. The results highlight the utility of semi-implicit measures in capturing nuanced attitudes toward immigration and avoiding the biases of traditional self-report methods. Implications for future research and social intervention are discussed, along with study limitations and potential improvements for the proposed tool.
LwHM: lightweight hybrid classifier for SDN-attack detection using recursive feature elimination
Internet connectivity has significantly enhanced the efficiency of daily operations, information retrieval, and global communication. However, this heightened reliance on technology has also exposed us to cybersecurity threats that are often beyond our control. Consequently, securing the data, privacy, and critical systems demands essential cybersecurity measures. This study focuses on the role of artificial intelligence in strengthening security systems to thwart network breaches. The study proposes a comprehensive three-part approach for software-defined networking (SDN) security. The first is that the concentration is on assuring data integrity and reliability for an SDN intrusion dataset. This involves critical steps such as data cleaning, preprocessing, and normalization. In the second step, six popular feature selection strategies are applied, which encompass recursive feature elimination (RFE), polynomial features, artificial neural networks, SelectKBest, least absolute shrinkage and selection operator (LASSO), and correlation-based features. These techniques help identify and incorporate significant and relevant features, thereby improving the overall model performance. The third part involves the creation of a lightweight hybrid model (LwHM) that leverages the strengths of k-nearest neighbors and decision tree models, utilizing a voting classifier. The LwHM surpasses the performance of the InSDN dataset, achieved an impressive accuracy score of 99.93% with RFE features, and enhance the SDN security efficiently.
Advanced Wafer Hotspot Detection through Image Segmentation and Stacked Model
The wafer map is a data visualization of a thin semiconductor fabric made of crystalline silicon, such as defects or test results. The wafer map is a base for creating electronic coordinate circuits and photovoltaic cells. During the wafer map production, any fault results in a product failure. The wafer map faults are undetectable to the naked eye, which is a big challenge. Hotspot detection in wafer maps is significantly important to evaluate the manufacturing process and. improve product yield. The hotspot detection in the wafer maps is the primary aim of this research. A novel wafer map hotspot detector (WHD) is proposed based on three stack fully connected conventional neural network layers and a dense layer. Data augmentation uses the segmented images of the wafers to build the proposed model. The proposed model is evaluated through several evalua-tion parameters and state-of-the-art studies comparative analysis. The proposed model achieved a 94% training and 90% testing performance accuracy for hotspot detection and shows better results than existing approaches. This study helps semiconductor engineers improve wafer manufacturing designs and efficiency in the semiconductor industry.
Consumption of ultra-processed foods is associated with cognitive status in elderly patients
Background: Emerging evidence suggests that there might be an association between excess consumption of ultra-processed foods (UPFs) on cognitive health. UPF intake could promote systemic inflammation, oxidative stress phenomena, and metabolic dysregulation, contributing to neurodegeneration onset and cognitive decline in elderly population.Aim: The aim of this cross-sectional study was to examine the relation between UPF dietary pattern on MCI status in elderly patients taking into account the contribution of inflammatory markers.Design: The dietary intake was assessed using a validated food frequency questionnaire in ninety-two participants. All reported food items were categorized according to the NOVA system, classifying foods on the basis of the extent and purpose of industrial processing. Plasmatic concentrations of TGF-β1 and TNF-ɑ were measured by ELISA assay at the time of baseline neuropsychological evaluation. The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) were administered to evaluate the cognitive function in all participants. Non-parametric tests, correlation analysis, and logistic regression models were performed to assess the relations between variables of interest.Results: No significant associations were observed for unprocessed/minimally processed foods, culinary processed foods, or processed foods across the different regression models. In contrast, higher consumption of UPF was associated with increased odds of MCI (adjusted OR = 4.24, 95% CI: 1.05–17.13). However, after additional adjustment for inflammatory biomarkers (TGF-β and TNF-α), the association was attenuated and no longer statistically significant (OR = 4.79, 95% CI: 0.73–31.24), although the direction of the association remained positive.Conclusion: UPF consumption may be associated with increased likelihood of MCI, and inflammatory status may potentially play a role in this association.