The "Healthcare Workers' Wellbeing [Benessere Operatori]" Project: A Longitudinal Evaluation of Psychological Responses of Italian Healthcare Workers during the COVID-19 Pandemic

Abstract

Background: COVID-19 forced healthcare workers to work in unprecedented and critical circumstances, exacerbating already-problematic and stressful working conditions. The "Healthcare workers' wellbeing (Benessere Operatori)" project aimed at identifying psychological and personal factors, influencing individuals' responses to the COVID-19 pandemic.

Methods: 291 healthcare workers took part in the project by answering an online questionnaire twice (after the first wave of COVID-19 and during the second wave) and completing questions on socio-demographic and work-related information, the Depression Anxiety Stress Scale-21, the Insomnia Severity Index, the Impact of Event Scale-Revised, the State-Trait Anger Expression Inventory-2, the Maslach Burnout Inventory, the Multidimensional Scale of Perceived Social Support, and the Brief Cope.

Results: Higher levels of worry, worse working conditions, a previous history of psychiatric illness, being a nurse, older age, and avoidant and emotion-focused coping strategies seem to be risk factors for healthcare workers' mental health. High levels of perceived social support, the attendance of emergency training, and problem-focused coping strategies play a protective role.

Conclusions: An innovative, and more flexible, data mining statistical approach (i.e., a regression trees approach for repeated measures data) allowed us to identify risk factors and derive classification rules that could be helpful to implement targeted interventions for healthcare workers.

Keywords: COVID-19; Random Effects/Expectation Maximization (RE-EM) Tree; healthcare workers; mental health; mixed effects model.

Figure 1

Radar charts displaying average scores of the investigated psychometric variables at T0 and T1, stratified by occupation (Table S1). Out of the 291 participants, 91 were physicians, 97 were nurses, 81 were other healthcare workers, and 22 were clerks. Abbreviation: DASS Depr, Depression Anxiety Stress Scale-Depression; DASS Anx, Depression Anxiety Stress Scale-Anxiety; DASS Stress, Depression Anxiety Stress Scale-Stress; MBI Prof Real, Maslach Burnout Inventory-Professional Realization; MBI Depers, Maslach Burnout Inventory-Depersonalization; MBI Emo Ex, Maslach Burnout Inventory-Emotional Exhaustion; STATE Ang, STATE Anger; IES Hypar, Impact of Event Scale- Hyperarousal; IES Avoid, Impact of Event Scale-Avoidance; IES Intr, Impact of Event Scale-Intrusion; ISI Tot, Insomnia Severity Index Total score.

Figure 2

RE-EM tree for the DASS-21 subscales. Depression and anxiety scales were square root transformed. Each tree represents a series of splits starting at the top of the tree. Starting from the top node, a series of questions are presented based on the splitting variables and corresponding cut-off values. Depending on the answer, other branches may appear until the final node, which displays the average predicted outcome value for participants, satisfying all the conditions, leading to that node and the proportion of subjects falling in the node itself. For example, in the first tree for the depression scale, the top split assigns observations having avoidant coping scores greater than or equal to 2.2 to the right branch. The predicted depression level for these subjects is given by the mean response value for the individuals in the data set with avoidant coping ≥ 2.2. For such subjects, the mean depression level is 4.4. Among subjects who have avoidant coping scores < 2.2, the working conditions also affect depression level. For subjects with avoidant coping score < 2.2 and working conditions ≥ 2.4, the predicted depression level is 2.9. For subjects having working conditions < 2.4 and avoidant coping scores between 1.7 and 2.2, the predicted depression level is 2.7, whereas for subjects having a working conditions score < 2.4 and avoidant coping scores lower than 1.7, the predicted depression level is 2. The same logic applies to all the other trees. Abbreviation: CONDWORK, working conditions; Psych History, psychiatric history.

Figure 3

Estimated regression trees for Emotional exhaustion, Insomnia, Intrusion, and State Anger. Square root transformation was applied to the MBI Emotional Exhaustion scale, ISI total score, and IES-R Intrusion scale, while ordered quantile normalization was used for the State Anger scale. Abbreviation: CONDWORK, working conditions.