International Journal of Psychological Research最新文献

This article presents the findings of a study that seeks to explore how im plementing Lean Management, a widely used form of flexible management, influences employees' subjective experiences at work. The study focuses on the changes and innovations by Lean Management in the technical and social aspects of work after its introduction in the Chilean retail industry. The study, which is both descriptive and analytical, is based on 26 interviews with industry consultants, managers, department heads, and union leaders. Using a work-clinic and socio-phenomenological framework, it illustrates how the industry's unique characteristics play a crucial role in understanding the process of work reorganization and the paradoxical realms of subjective experience it has created. The article also discusses how these contradictions are influenced by the broader social, labor, and business context of neoliberal modernization in Chilean society.

Stroke, the second leading cause of death globally, necessitates prompt diagnosis for effective prognosis. CT imaging has limitations, especially in identifying acute lesions. This work introduces a novel deep repre sentation that uses multimodal inputs from CT studies and perfusion parametric maps, to retrieve stroke lesions. The architecture follows an autoencoder representation that forces attention on the geometry of stroke through additive cross-attention modules. Besides, a cascade train is herein proposed to generate synthetic perfusion maps that complement multimodal inputs, refining stroke lesion segmentation at each stage of processing and supporting the observational expert analysis. The proposed approach was validated on the ISLES 2018 dataset with 92 studies; the method outperforms classical techniques with a Dice score of .66 and a precision of .67.

The objectives of this study were to develop the Laboratory of Immersive Learn ing in Health and Nursing - LIASE, based on the main themes of biosafety in health, and to evaluate the learning process of undergraduate nursing students from a public federal university through portable Electroencephalogram (EEG) Emotiv Insight 2.0, observation, and Intrinsic Motivation Inventory. The present research contains a qualitative-quantitative, exploratory, and experimental methodology from a pilot virtual laboratory, developed in the Immersive Virtual World - IVW, represented in this study by Second Life - SL. The sample consisted of 17 students who agreed with the inclusion criteria of the study. Among them, 9 students had stable EEG signals. Those students were observed during the monitoring of brain activity by the EEG, and at the end of the proposed learning pathway, they filled out the Intrinsic Motivation Inventory (IMI). The results were obtained through triangulation of the different collec tion instruments and the following variables: Stress, Enthusiasm/Excitement, Engagement, Focus/attention, and Relaxation, measured and verified by the Emotiv algorithm's brain wave analysis, which algorithm which correspond to the metrics of brain performance.

Parkinson's disease (PD) is a common neurodegenerative disorder worldwide, with over 6.2 million registered cases. Gait analysis plays a fundamental role in evaluating motor abnormalities associated with this disease. However, current methods, such as marker-based systems, are intrusive and expert-dependent. Markerless alternatives, like video sequence analysis, have been proposed, but they tend to provide overall classification scores and lack the ability to interpret joint kinematics in detail. An innovative technique is presented using volumetric convolutional networks that can learn intermediate postural patterns and distinguish between Parkinson's patients and control subjects. This approach utilizes OpenPose activations and then applies hierarchical convolution to minimize classification. In tests conducted with 14 Parkinson's patients and 16 control subjects, this method achieved a classification accuracy of 98%.

Despite institutional efforts, reconciliation among former actors of the Colombian armed conflict has yet to be achieved, with prejudice being one direct driver of this drawback. We present an EEG-based functional connectivity study applied to four groups of former actors who completed an Implicit Association Test designed to measure prejudice toward victims or combatants. We analyzed seven measures of functional connectivity calculated in six different frequency bands and two experimental conditions. In the behavioral task, we found more prejudice toward victims from the same victims and more prejudice of civilians toward combatants. For the connectivity measures, we found differences in theta band among the victims' and ex-paramilitaries' groups concerning the civilians' and ex-guerrillas' groups, and differences in the beta2 band among the victims' and ex-guerrillas' groups concerning the ex-paramilitaries' group. The results help us design more effective socio-cognitive interventions to reduce prejudice.

The pursuit of a unified theory that captures the intricacies of the brain and mind continues to be a significant challenge in theoretical neuroscience. This paper presents a novel, triune framework that utilizes the concept of collective biased random walk (cBRW). Our approach strives to transcend biological specifics, offering a high-level abstraction that remains general and applicable across various neural phenomena. Despite the solid traditional foundation of computational neuroscience, the intricate delicacy of neural processes calls for a renewed probabilistic approach. We aim to utilize the intuitive nature of probability concepts -such as the probability of localization and state, and uniform probability distribution- to study the stochastic organization of electric charges and signals in the brain. This electrophysiological intricacy emerges from the seemingly paradoxical reality that tiny electric events, while random, collectively give rise to predictable, long-range oscillations. These oscillations manifest in three groups of activation states. Our framework categorizes the brain as a triune system, accommodating classical, semiclassical, and non-classical interpretations of both probabilistic phenomena and cBRW models, alongside three groups of states. We conclude that by appreciating, rather than overlooking, the tiny random walks of electric charges and signals in the brain, we can gain a triune mathematical foundation for theoretical brain science, the powerful capabilities of this organ, and the electromagnetic interfaces we can develop.

Purpose: The ability to see and process images depends on the function of the eyes and the processing of visual information by neurons in the cerebral cortex, something that could be measured through electroencephalography (EEG). Although the EEG is used to evaluate visual pathways in children and demyelination diseases, the limited utilization of brain recording techniques in other applications like therapy is primarily due to budget constraints. The goal of this paper is to demonstrate results from studying brain aspects of vision, utilizing measurements based on oscillatory activity analysis, low-cost, portable equipment, and a processing pipeline relying on Python's open-source libraries. These studies involve healthy subjects who wear glasses to assess changes in visual perception.

Methods: First, electroencephalographic signals were recorded while the subjects observed a visually standardized stimulus. The signals were processed and filtered to reduce artifacts, and the power spectral density (PSD) was calculated to observe the presence of steady-state visual potentials (VEP) to confirm the capture of neuronal activation to the visual stimulus.

Results: It was possible to establish a difference between subjects wearing and not wearing their glasses, allowing validation that the information acquired with the transferable equipment is adequate for the analysis of neuronal activity related to visual processing, opening the possibility to be used in future studies in therapy.

Conclusion: This study contributes to the development of cost-effective and portable EEG solutions for visual system analysis. It demonstrates the potential for applying transferable EEG devices in clinical settings and highlights the importance of tailored visual stimuli for reliable neural activation.

Epilepsy is a worldwide public health issue, given its biological, social, and economic impacts. Considering several open questions about synchronization and desynchronization mechanisms underlying epileptic phenomena, the development of algorithms and computational toolboxes for such analysis is highly relevant to their research. Moreover, given the recent developments of neurotechnology for epilepsy, it is essential to understand that proposals like computational tools may provide consistent data for closed-loop control systems, necessary in neuromodulation treatment alternatives, and for real-time monitoring systems to predict the occurrence of epileptic seizures. In the present work, SynchroLINNce, a freely distributable MATLAB toolbox designed to be used by epilepsy neuroscientists, including software-untrained), is proposed. Among its features, several functionalities such as recording visualization, digital filtering, and correlation analysis, as well as more specific methodologies, such as mechanisms for the automatic detection of epileptiform spikes, morphology analysis of these spikes, and their coincidence between channels are presented.

Background: Research regarding the effects of discrimination on suicidal thoughts of migrant youth is scarce. For this reason, the present study sought to examine the relationship between experiences of discrimination and suicidal ideation in migrant boys and girls considering as mediating variables the Problems with Classmates and Integration and Social Competence.

Methods: A cross-sectional design was used for the study, which considered a sample of 198 young migrants in Chile, 101 (51%) of whom were male and 97 (49%) females, aged between 9 and 13 years (ME = 10.18; SD = .92). A multiple mediation analysis was carried out.

Results: The estimated model indicated that discrimination is positively related to suicidal ideation and that this relationship is fully mediated by peer problems and social integration and competence of migrant children.

Conclusion: Our findings demonstrate the importance of addressing and preventing discrimination within the classroom. Migrants who experience discrimination can be severely affected, with serious consequences for their mental health. Actively promoting the inclusion of migrant children could help to combat discrimination within the school context.

The Family Well-Being Index (FMWB) is a broadly used instrument. However, it has not been tested for any latent structure via any factor analysis. This study aimed to check the internal structure of the FMWB by implementing 4-factor models in a Colombian sample from 3 different regions. A validity evidence study was conducted, and a sample of 524 nuclear families was obtained (59.2% Bogotá, 15.3% Santa Marta, 25.6% Cali-Palmira). The data was analyzed by organizing the answer in a univariate and multivariate matrix arrangement, using the software factor and R, the models were tested; the best fitting model was the bi-factor model (x2 =404.127, df = 228, CFI = .958, TLI= .949, RMSEA= .047, SRMR= .055). The results suggest that the bi-factor model is the best model that explains the latent structure of the FMWB, by comprehending each member's responses or perspectives as latent dimensions (fathers, mothers, children) of the FMWB More research is needed to obtain more validity evidences that could help justify correctly the scores obtained with this scale.