Determinants of burnout and other aspects of psychological well-being in healthcare workers during the Covid-19 pandemic: A multinational cross-sectional study

This study did not test a specific intervention or treatment. Instead, it was an observational study designed to explore the associations between various personal and work-related characteristics and the psychological well-being of healthcare workers during the initial phase of the COVID-19 pandemic. The researchers aimed to identify factors that predicted or were correlated with burnout, anxiety, and depression.

Specifically, they examined how the following characteristics were related to psychological outcomes:

**Job role:** Whether a healthcare worker was a doctor, nurse, or in another clinical or non-clinical role, particularly focusing on patient-facing responsibilities.

**Redeployment:** Whether a healthcare worker had been reassigned to a different role or department due to the pandemic.

**SARS-CoV-2 testing:** Whether a healthcare worker had been tested for the virus.

**Safety attitudes/culture:** The healthcare worker's perception of safety within their workplace, including their comfort in speaking up about safety concerns and their belief in the organization's commitment to safety.

**Gender:** The self-reported gender of the participant.

The primary outcome measures were:

**Burnout:** A state of emotional, physical, and mental exhaustion caused by prolonged or excessive stress.

**Anxiety:** A feeling of worry, nervousness, or unease, typically about an event or something with an uncertain outcome.

**Depression:** A mood disorder causing a persistent feeling of sadness and loss of interest.

The study also looked at the interrelationships between these psychological outcomes, for example, whether anxiety and depression predicted burnout, or vice-versa.

The study included a large sample of **3,537 healthcare workers**.

The participants were recruited from three different countries:

The **United Kingdom**

**Poland**

**Singapore**

All participants were actively working in healthcare settings during the data collection period, which spanned from **March 22, 2020, to June 18, 2020**. This timeframe corresponds to the early and intense phase of the global COVID-19 pandemic, when healthcare systems were under significant pressure and there was considerable uncertainty regarding the virus and its management. The study focused on individuals directly involved in the healthcare response to the pandemic. The abstract does not specify age ranges, specific health statuses (e.g., pre-existing conditions), or other demographic details beyond job role and gender.

The researchers used a **self-administered online questionnaire** to collect data from the healthcare workers. This method allowed for broad reach across multiple countries during a period of restricted movement. The questionnaire comprised several standardized and validated scales to assess the key variables:

**Safety Attitudes Questionnaire (SAQ):** This instrument was used to evaluate the participants' perceptions of safety culture within their workplace. The SAQ typically assesses various domains related to safety, such as teamwork climate, job satisfaction, perceptions of management, safety climate, stress recognition, and working conditions. A higher score on the SAQ generally indicates a more positive perception of safety culture. The abstract specifically mentions using "bottom quartile SAQ score" and "top quartile SAQ score" as predictors, indicating that participants' scores were ranked, and those in the lowest 25% (poor safety attitudes) and highest 25% (good safety attitudes) were compared.

**Oldenburg Burnout Inventory (OLBI):** This scale was used to measure burnout. The OLBI assesses two core dimensions of burnout:

* **Exhaustion:** Feelings of physical, cognitive, and emotional depletion.

* **Disengagement:** A negative or cynical attitude towards one's work and a withdrawal from work-related interactions.

The OLBI is designed to be culturally adaptable and is widely used in occupational health research. The abstract indicates that participants were "screened positive for burnout," implying a specific cutoff score on the OLBI was used to categorize individuals as experiencing burnout.

**Hospital Anxiety and Depression Scale (HADS):** This scale was used to screen for symptoms of anxiety and depression. The HADS is a self-report questionnaire consisting of 14 items, with 7 items related to anxiety (HADS-A) and 7 items related to depression (HADS-D). Each item is scored from 0 to 3, giving a maximum score of 21 for anxiety and 21 for depression. Higher scores indicate greater levels of anxiety or depression. The HADS is commonly used in medical settings because it specifically focuses on the psychological symptoms of anxiety and depression, minimizing overlap with physical symptoms that might be related to a medical condition rather than a mood disorder. Similar to burnout, participants were "screened positive for anxiety" or "screened positive for depression," suggesting specific cutoff scores were applied to the HADS-A and HADS-D subscales.

In addition to these scales, the questionnaire also collected demographic and work-related information, such as job role, redeployment status, gender, and whether the individual had been tested for SARS-CoV-2.

This study employed a **multinational cross-sectional design**. This means that data was collected from a diverse group of participants (healthcare workers in the UK, Poland, and Singapore) at a single point in time, or over a relatively short, defined period (March 22 to June 18, 2020). The researchers distributed a self-administered questionnaire to gather information on various personal characteristics, work conditions, safety perceptions, and psychological well-being simultaneously.

**How they ran the study:**

Healthcare workers were invited to participate in an online survey during the specified three-month window. The survey included the SAQ, OLBI, and HADS, along with questions about their job role, redeployment status, gender, and SARS-CoV-2 testing history. Once all data was collected, it was analyzed using **multivariate logistic regression**. This statistical technique is used to model the probability of a binary outcome (e.g., screening positive for burnout vs. not) based on one or more predictor variables (e.g., job role, safety attitudes). It allows researchers to determine the independent effect of each predictor while controlling for the influence of other variables.

**Why this design matters:**