Depression, suicide risk, and workplace bullying: a comparative study of fly-in, fly-out and residential resource workers in Australia

2020 ◽  
Vol 44 (2) ◽  
pp. 248 ◽  
Author(s):  
Peta Miller ◽  
Libby Brook ◽  
Norman J. Stomski ◽  
Graeme Ditchburn ◽  
Paul Morrison
Keyword(s):  
Social Support ◽  
Linear Model ◽  
Suicide Risk ◽  
Workplace Bullying ◽  
Protective Factor ◽  
General Linear Model ◽  
Psychosocial Distress ◽  
Clinical Depression ◽  
General Linear ◽  
General Linear Model Analysis

Objectives The primary objective of this study was to establish whether clinical depression and increased suicide risk differed between Australian fly-in, fly-out (FIFO) workers and their residential counterparts in the resources sector. We also sought to identify whether bullying and social support were associated with depression and suicide risk in this cohort. Methods A cross-sectional survey design was used. Completed questionnaires were received from 751 respondents who were employed in the Australian resources sector. Primary outcomes were assessed with the Beck Depression Inventory, Beck Hopelessness Scale and Negative Acts Questionnaire – Revised. A general linear model was used to examine the association between depression, hopelessness and predictive factors. Results The results of a general linear model analysis demonstrated that depression (partial η2 = 0.02; P = 0.01) and hopelessness (partial η2 = 0.02; P = 0.02) were significantly higher in residential than FIFO workers. In addition, bullying was significantly associated with higher levels of depression (partial η2 = 0.11; P = 0.001) and hopelessness (partial η2 = 0.04; P = 0.001). Finally, increased social support was significantly associated with lower depression rates (partial η2 = 0.13; P = 0.001) and hopelessness (partial η2 = 0.14; P = 0.001). Conclusions Interventions should be delivered to reduce depression and hopelessness in Australian resource workers, particularly through addressing workplace bullying and enhancing social support. What is known about the topic? One-third of Australian resource workers experience psychosocial distress. Research examining factors that influence such distress is lacking. What does this paper add? The results of this study demonstrate that clinical depression and suicide risk were significantly higher in residential resource workers than in FIFO workers. Depression and suicide risk were directly associated with workplace bullying. In addition, social support was an important protective factor against depression and bullying in Australian FIFO and residential resource workers. What are the implications for practitioners? There is an urgent need to introduce interventions that reduce psychosocial distress in the resource sector, particularly through the mitigation of bullying

General linear model analysis of behavioural responses of Boerand Tswana goats to successive handling

2016 ◽  
Author(s):  
G. Molale ◽  
M. A. Antwi ◽  
J. N. Lekunze ◽  
U. Luvhengo
Keyword(s):  
Linear Model ◽  
General Linear Model ◽  
Model Analysis ◽  
Flight Time ◽  
Behavioural Response ◽  
Flight Speed ◽  
General Linear ◽  
Behavioural Responses ◽  
General Linear Model Analysis ◽  
Boer Goats

The purpose of this experiment was to evaluate the behavioural response of Boer goats and Tswana goats to successive handling. Boer goats and Tswana goats were subjected to aging and tail restrain during handling. Behavioural response was recorded through the vocalisation score (VS), crush score (CS), flight speed (FS), and flight time (FT) scores twice a week for eight weeks. A total of thirty six Boer goats and Tswana goats were used in this experiment. The results indicated that the Tswana goats were the most temperament (p < 0.05) and Boer goats were the calmest. Behavioural scores significantly increased throughout the experiment.

scholarly journals A population-based study of health-promoting behaviors and their predictors in Iranian males, 2019

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Fovziye Sanaati ◽  
Mehrnaz Geranmayeh ◽  
Zahra Behboodi Moghadam ◽  
Armin Zareiyan ◽  
Keshvar Samadaee Gelehkolaee ◽  
...  
Keyword(s):  
Social Support ◽  
Linear Model ◽  
General Linear Model ◽  
Influential Factor ◽  
General Linear ◽  
Cluster Sampling ◽  
Cross Sectional ◽  
Health Promoting Behaviors ◽  
Health Promoting ◽  
Health Promoting Lifestyle

Abstract Background Health-promoting lifestyle (HPL) is any measure taken to maintain a person’s health. The most important and influential factor in maintaining and enhancing health are health-promoting behaviors (HPB). This study aimed to determine HPB and their predictors among Iranian men. Methods In this cross-sectional study, 783 Iranian men, living in Tehran, were selected, using multistage cluster sampling. The employed questionnaires, namely the sociodemographic questionnaire, Health-Promoting Lifestyle Profile II (HPLP-II), and the second part of the Personal Resource Questionnaire (PRQ 85-Part 2), were completed through interviews. The relationship between the dependent variables (HPLP-II and its subdomains) and independent variables (sociodemographic characteristics and social support) was investigated using the adjusted General Linear Model (GLM). Results The mean ± standard deviation of the total HPLP-II score was 2.72 ± 0.44 in the range of 1–4. Among the six dimensions of the HPB, the participants achieved the highest score (3.00 ± 0.52) and lowest score (1.96 ± 0.56) in spiritual growth and physical activity, respectively. The Pearson test showed that the perceived social support was significantly correlated with HPLP-II (r = 0.23; p < 0.001) and all of its subdomains (r = 0.09–0.24; p < 0.001). Based on the adjusted general linear model, social support, age, job, and income adequacy were the predictors of HPL in men and could explain 30.9% of the variance of the HPL score. Conclusions The research findings confirmed the importance of social support and modifying variables (social and personal) in the incidence of HPB in men.

scholarly journals ENHANCED ACADEMICIANS ENGAGEMENT: EVIDENCE FROM GAMIFICATION INTERVENTIONS IN HIGHER EDUCATION INSTITUTES

2020 ◽  
Vol 7 (9) ◽  
pp. 24-30
Author(s):  
KDV Prasad ◽  
Mruthyanjaya Rao Mangipudi
Keyword(s):  
Linear Model ◽  
Course Completion ◽  
General Linear Model ◽  
Model Analysis ◽  
General Linear ◽  
Teaching Staff ◽  
General Linear Model Analysis ◽  
E Learning ◽  
Survey Instruments ◽  
Game Context

This paper presents An outcome of gamification interventions in higher educational institutes to enhance the engagement of the teaching staff, the academicians. The academicians' engagement is an important component to enhance learning and timely course completion and for excellent student outcomes. In the recent past the gamification being used as a beneficial strategy in several areas such as business, education, e-learning, research and development and behavioral change of employees. The authors have applied gamification interventions on Academicians of Engineering colleges around Hyderabad to see the effect of gamification on academician's motivation, engagement, loyalty, dedication to the organization, and turnover. A group of 150 academicians, the teaching staff were tested using before gamification and post-gamification survey instruments, a questionnaire. The reliability of the survey instrument assessed measuring Cronbach-alpha statistics, descriptive statistics, and General Linear Model analysis was carried out to predict the influence of gender and academician’s gamification choices on study variables. The post-gamification results reveal a statistically significant influence on motivation, engagement, whereas gamification intervention is a not good predictor of employee turnover and loyalty. The results from the General Linear Model analysis indicate the use of games in non-game context immensely motivates the employees and academicians inclined towards quests and challenges.

scholarly journals Modelling Impulse Response Function of Functional Infrared Imaging for General Linear Model Analysis of Autonomic Activity

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 849 ◽  
Author(s):  
David Perpetuini ◽  
Daniela Cardone ◽  
Chiara Filippini ◽  
Antonio Chiarelli ◽  
Arcangelo Merla
Keyword(s):  
Linear Model ◽  
Impulse Response ◽  
Infrared Imaging ◽  
Thermal Response ◽  
General Linear Model ◽  
General Linear ◽  
Autonomic Arousal ◽  
Autonomic Activity ◽  
General Linear Model Analysis ◽  
Time Invariance

Functional infrared imaging (fIRI) is a validated procedure to infer autonomic arousal. Currently, fIRI signals are analysed through descriptive metrics, such as average temperature changes in a region of interest (ROI). However, the employment of mathematical models could provide a powerful tool for the accurate identification of autonomic activity and investigation of the mechanisms underlying autonomic arousal. A linear temporal statistical model such as the general linear model (GLM) is particularly suited for its simplicity and direct interpretation. In order to apply the GLM, the thermal response linearity and time-invariance of fIRI have to be demonstrated, and the thermal impulse response (TIR) needs to be characterized. In this study, the linearity and time-invariance of the thermal response to sympathetic activating stimulation were demonstrated, and the TIR for employment of the GLM was characterized. The performance of the GLM-fIRI was evaluated by comparison with the GLM applied on synchronous measurements of the skin conductance response (SCR). In fact, the GLM-SCR is a validated procedure to estimate autonomic arousal. Assuming the GLM-SCR as the gold standard approach, a GLM-fIRI sensitivity and specificity of 86.4% and 75.9% were obtained. The GLM-fIRI may allow increased performances in the evaluation of autonomic activity and a broader range of application of fIRI in both research and clinical settings for the assessment of psychophysiological and psychopathological states.

Nicotine Effects on Brain Function during a Visual Oddball Task: A Comparison between Conventional and EEG-informed fMRI Analysis

2012 ◽  
Vol 24 (8) ◽  
pp. 1682-1694 ◽  
Author(s):  
Tracy Warbrick ◽  
Arian Mobascher ◽  
Jürgen Brinkmeyer ◽  
Francesco Musso ◽  
Tony Stoecker ◽  
...  
Keyword(s):  
Linear Model ◽  
Brain Function ◽  
General Linear Model ◽  
Fmri Data ◽  
General Linear ◽  
Single Trial ◽  
Conventional Analysis ◽  
General Linear Model Analysis ◽  
Bold Responses ◽  
Fmri Analysis

In a previous oddball task study, it was shown that the inclusion of electrophysiology (EEG), that is, single-trial P3 ERP parameters, in the analysis of fMRI responses can detect activation that is not apparent with conventional fMRI data modeling strategies [Warbrick, T., Mobascher, A., Brinkmeyer, J., Musso, F., Richter, N., Stoecker, T., et al. Single-trial P3 amplitude and latency informed event-related fMRI models yield different BOLD response patterns to a target detection task. Neuroimage, 47, 1532–1544, 2009]. Given that P3 is modulated by nicotine, including P3 parameters in the fMRI analysis might provide additional information about nicotine effects on brain function. A 1-mg nasal nicotine spray (0.5 mg each nostril) or placebo (pepper) spray was administered in a double-blind, placebo-controlled, within-subject, randomized, cross-over design. Simultaneous EEG-fMRI and behavioral data were recorded from 19 current smokers in response to an oddball-type visual choice RT task. Conventional general linear model analysis and single-trial P3 amplitude informed general linear model analysis of the fMRI data were performed. Comparing the nicotine with the placebo condition, reduced RTs in the nicotine condition were related to decreased BOLD responses in the conventional analysis encompassing the superior parietal lobule, the precuneus, and the lateral occipital cortex. On the other hand, reduced RTs were related to increased BOLD responses in the precentral and postcentral gyri, and ACC in the EEG-informed fMRI analysis. Our results show how integrated analyses of simultaneous EEG-fMRI data can be used to detect nicotine effects that would not have been revealed through conventional analysis of either measure in isolation. This emphasizes the significance of applying multimodal imaging methods to pharmacoimaging.

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