The prevalence of effort-reward imbalance and its associations with working conditions, psychosocial resources and burden among health care workers during the COVID-19 pandemic: Results of the egePan-Voice study

ObjectiveThe association between a measure of effort-reward imbalance (ERI) and profession as well as gender in a sample of health care workers (HCW) during the first wave of the COVID-19 pandemic in Germany using the egePan-Voice study. In addition, we examined, which factors are associated with an effort-reward imbalance ratio (ERI ratio) &gt;1.MethodsIn a large sample of HCW (N = 6174) we assessed occupational stress with the short version of the effort-reward imbalance (ERI) questionnaire, working conditions, COVID-19-related problems and psychosocial resources (ENRICHD Social Support Inventory, ESSI; Sense of Coherence Scale, SOC-3 and optimism, SOP2).ResultsThe prevalence of a ERI ratio &gt;1 among HCW was 50.9%. The prevalence’s of an ERI ratio &gt;1 were statistically significant different between gender as well as the occupational profession. The proportion of women (51.8%) with ERI ratio &gt;1 was significantly higher than among men (47.8%). The highest ERI imbalance was found among nurses (62.8%), followed by medical technical assistants (MTA) (58.8%), while psychologists/psychotherapists revealed the lowest value (37.8%), followed by physicians (41.8%). In the total sample, most essential factors reported at this time for increased ERI ratio were: insufficient staff for the current work load, insufficient recovery, feeling insufficiently protected by measures taken by the hospital/the employer, high occupancy rate of the wards, insufficient trust in colleagues and being a nurse as compared with being a physician.ConclusionThe findings indicate a high proportion of HCW with effort-reward imbalance and substantial profession-related differences. Preventive interventions should be offered to vulnerable groups among the HCW to decrease the imbalance measured by work stress.</jats:sec

Correlations of the “Work–Family Conflict” with occupational stress—a cross-sectional study among university employees

Background: The working conditions at universities and hospitals are reported to be stressful. Several national and international studies have investigated occupational stress in hospitals. However, scientific studies at colleges and universities addressing psycho-social stress factors and their potential consequences are scarce. In this context, the consequences and correlations of the factor of work–family conflict, in particular, are currently uninvestigated. The aim of our study was to assess data on psychosocial stress in the context of the compatibility of work and family. Methods: Data were gathered through a cross-sectional-study, N = 844 (55% female, 41% male), on university staff (42.3% scientists, 14.3% physicians, 19.4% employees in administration, and 19.3% employees in service). Participants filled out questionnaires to provide their personal data and details of their work and private life conditions. For this purpose, we used the Work–Family and Family–Work Conflict Scales, Effort-Reward Inventory and Overcommitment Scale (ERI, OC), Patient Health Questionnaire (PHQ-4), short-form Maslach Burnout Inventory (MBI), and questions on their subjective health. Statistical analyses were performed using SPSS 22. Results: We found high levels of stress parameters in the total sample: extra work (83%), fixed-term work contracts (53%), overcommitment (OC, 26%), Effort-Reward Imbalance (18%, ERI Ratio > cut-off 0.715), work–family conflict (WFC, 35%), and family–work conflict (FWC, 39%). As hypothesized, we found significant correlations of both WFC and FWC with psychosocial work strain (ERI Ratio) as well as overcommitment (OC). Mental and somatic health parameters also had a significant positive correlation with WFC and FWC. Using a regression analysis (N = 844), we identified WFC as a predictor of burnout, while emotional exhaustion, extra work, and overcommitment could be identified as predictors of WFC and FWC. Discussion: The results of our study point toward deficits in the compatibility of work life and private life in the work fields of science, colleges, and universities. Furthermore, we found indicators that work–family conflicts (interrole conflicts) have an impact on mental and somatic health. These work–family conflicts should be targets for preventions and interventions with the aim of improving the work-life balance and mental and somatic wellbeing of employees

Recognition of Emotional Facial Expressions and Alexithymia in Patients with Chronic Facial Pain

Objectives Alexithymia, conceived as difficulties to identify emotions, is said to be related with several pain syndromes. This study examined the recognition of facially expressed emotions and its relation to alexithymia in subjects with chronic facial pain. Methods A total of 62 subjects were recruited, with n=20 patients with chronic facial pain and n=42 healthy controls. All subjects were tested for the recognition of facially expressed emotions (Facially Expressed Emotion Labelling Test (FEEL test). The Toronto Alexithymia Scale (TAS-26) was used for the diagnosis of alexithymia. Results Patients with chronic facial pain performed worse than controls at the FEEL task (p<.001) and showed higher total TAS scores (p<.001). This indicates the presence of alexithymia and facial emotion recognition deficits in the facial pain group. Discussion It was concluded from the results that both the recognition of facially expressed emotions, and the ability to identify and describe one’s own feelings (TAS), are restricted in chronic orofacial pain patients. This relationship is particularly important in the treatment of chronic facial pain, indicating that it should become part of the treatment in addition to the therapeutic key issues, to influence the quality of life of the affected patients positively

Head movements and postures as pain behavior.

Pain assessment can benefit from observation of pain behaviors, such as guarding or facial expression, and observational pain scales are widely used in clinical practice with nonverbal patients. However, little is known about head movements and postures in the context of pain. In this regard, we analyze videos of three publically available datasets. The BioVid dataset was recorded with healthy participants subjected to painful heat stimuli. In the BP4D dataset, healthy participants performed a cold-pressor test and several other tasks (meant to elicit emotion). The UNBC dataset videos show shoulder pain patients during range-of-motion tests to their affected and unaffected limbs. In all videos, participants were sitting in an upright position. We studied head movements and postures that occurred during the painful and control trials by measuring head orientation from video over time, followed by analyzing posture and movement summary statistics and occurrence frequencies of typical postures and movements. We found significant differences between pain and control trials with analyses of variance and binomial tests. In BioVid and BP4D, pain was accompanied by head movements and postures that tend to be oriented downwards or towards the pain site. We also found differences in movement range and speed in all three datasets. The results suggest that head movements and postures should be considered for pain assessment and research. As additional pain indicators, they possibly might improve pain management whenever behavior is assessed, especially in nonverbal individuals such as infants or patients with dementia. However, in advance more research is needed to identify specific head movements and postures in pain patients

Head posture time series with corresponding video frames showing reaction to a painful heat stimulus from BioVid dataset (high temperature plateau lasting from second 0 to 3.5).

<p>The subject moves her head downwards and followed by a little upward movement, which is reflected by the increase and following decrease of the pitch angle.</p

Overview on analyzed datasets.

<p>In each dataset, the same subjects underwent painful trials and control trials. For the BioVid dataset, several videos were excluded from analyses, because participants left the camera’s field of view or visual review revealed obvious pose measurement errors. Abbreviations: M = mean, SD = standard deviation.</p

Specific head posture’s occurrence counts and significance test results.

<p>Each dataset was subdivided in 27 postures. The 8 postures that occurred most frequently among pain trials were considered for comparing the frequency of occurrences in pain and control trials with binomial tests. The figure illustrates the 8 postures per dataset and lists the occurrence frequencies in the trial categories. Significant differences are marked by asterisks.</p

Head movement clusters in the BioVid dataset with number of pain and control trials falling into the cluster.

<p>Clusters are illustrated by their mean movement. Significant differences (according to the conduced binomial tests) are marked by asterisks.</p

Egocentric rotation angles describing orientation of the head in degrees (DEG).

<p>Pitch quantifies down- or upward head orientation, yaw quantifies left or right head turn, and roll quantifies right or left head tilt.</p