Although numerous studies have addressed the impact of the COVID-19 lock-downs on psychological distress, scarce data is available relating to the role of Present-Hedonistic (PH) time perspective and gender differences in the development of depressive symptoms and stress during the period of strict social distancing. We hypothesized that gender would moderate the relationship between PH and depressiveness or stress levels, such that PH would negatively correlate with psychological distress in women but correlate positively in men. The present study was online and questionnaire-based.  = 230 participants aged 15-73 from the general population took part in the study. The results of moderation analysis allowed for full acceptance of the hypothesis for depression as a factor, but for stress the hypothesis was only partially confirmed, since the relationship between PH time perspective and stress was not significant for men (although it was positive, as expected). The findings are pioneering in terms of including PH time perspective in predicting psychological distress during the COVID-19 lock-down and have potentially significant implications for practicing clinicians, who could include the development of more adaptive time perspectives and balance them in their therapeutic work with people experiencing lock-down-related distress.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7521869PMC
http://dx.doi.org/10.1016/j.paid.2020.110395DOI Listing

Publication Analysis

Top Keywords

time perspective
16
psychological distress
12
present-hedonistic time
8
depressive symptoms
8
symptoms stress
8
covid-19 lock-down
8
time
5
stress
5
gender moderator
4
moderator present-hedonistic
4

Similar Publications

Background: Viscosupplementation is a viable alternative for managing knee osteoarthritis, showing potential to delay the need for total joint replacement in affected patients.

Methods: We constructed an economic model that compared viscosupplementation with hylan G-F 20, with available hyaluronic acids, and no viscosupplementation over a 5-year period, from the perspective of the Colombian general health system. Time until total knee replacement, sourced from published literature, informed the model.

View Article and Find Full Text PDF

This study aimed to assess the immediate effects of transcutaneous spinal direct current stimulation (tsDCS) on pain outcomes, measured using the visual analog scale (VAS) and pressure pain thresholds in a cohort of 55 participants experiencing chronic pain using a controlled, randomized trial with 55 participants allocated into 2 groups: 2 mA and 0.5 mA of tsDCS for 20 min. Anodal stimulation was applied on the 12th thoracic vertebra, with the cathode positioned on the 7th cervical vertebra.

View Article and Find Full Text PDF

The Trapping Mechanism at the AlGaN/GaN Interface and the Turn-On Characteristics of the p-GaN Direct-Coupled FET Logic Inverters.

Nanomaterials (Basel)

December 2024

State Key Laboratory of ASIC and System, Shanghai Institute of Intelligent Electronics & Systems, School of Microelectronics, Fudan University, Shanghai 200433, China.

The trapping mechanism at the AlGaN/GaN interface in the p-GaN high electron mobility transistors (HEMTs) and its impact on the turn-on characteristics of direct-coupled FET logic (DCFL) inverters were investigated across various supply voltages () and test frequencies (). The frequency-conductance method identified two trap states at the AlGaN/GaN interface (trap activation energy - ranges from 0.345 eV to 0.

View Article and Find Full Text PDF

In this paper, we face the point-cloud segmentation problem for spinning laser sensors from a deep-learning (DL) perspective. Since the sensors natively provide their measurements in a 2D grid, we directly use state-of-the-art models designed for visual information for the segmentation task and then exploit the range information to ensure 3D accuracy. This allows us to effectively address the main challenges of applying DL techniques to point clouds, i.

View Article and Find Full Text PDF

DAT: Deep Learning-Based Acceleration-Aware Trajectory Forecasting.

J Imaging

December 2024

School of Innovation, Design and Technology (IDT), Mälardalen University, 72123 Västerås, Sweden.

As the demand for autonomous driving (AD) systems has increased, the enhancement of their safety has become critically important. A fundamental capability of AD systems is object detection and trajectory forecasting of vehicles and pedestrians around the ego-vehicle, which is essential for preventing potential collisions. This study introduces the Deep learning-based Acceleration-aware Trajectory forecasting (DAT) model, a deep learning-based approach for object detection and trajectory forecasting, utilizing raw sensor measurements.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!