Unmet Psychosocial Needs of Health Care Professionals in Europe During the COVID-19 Pandemic: Mixed Methods Approach
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic severely affected everyday life and working conditions for most Europeans, particularly health care professionals (HCPs). Over the past 3 years, various policies have been implemented in various European countries. Studies have reported on the worsening of mental health, work-related stress, and helpful coping strategies. However, having a closer look is still necessary to gain more information on the psychosocial stressors and unmet needs of HCPs as well as nonmedical staff. OBJECTIVE: This study aimed to obtain quantitative information on job-related stressors of physicians and nurses and the coping strategies of HCPs and nonmedical staff at 2 periods of the COVID-19 pandemic. By further analyzing qualitative comments, we wanted to gain more information on the psychosocial stressors and unmet needs of HCPs as well as nonmedical staff on different levels of experience. METHODS: A cross-sectional survey was conducted at 2 time points during the COVID-19 pandemic in several European countries. The first study period (T1) lasted between April 1 and June 20, 2020, and the second study period (T2) lasted between November 25, 2021, and February 28, 2022. On a quantitative level, we used a questionnaire on stressors for physicians and nurses and a questionnaire on coping strategies for HCPs and nonmedical staff. Quantitative data were descriptively analyzed for mean values and differences in stressors and coping strategies. Qualitative data of free-text boxes of HCPs and nonmedical staff were analyzed via thematic analysis to explore the experiences of the individuals. RESULTS: T1 comprised 609 participants, and T2 comprised 1398 participants. Overall, 296 participants made 438 qualitative comments. The uncertainty about when the pandemic would be controlled (T1: mean 2.28, SD 0.85; T2: mean 2.08, SD 0.90) and the fear of infecting the family (T1: mean 2.26, SD 0.98; T2: mean 2.02, SD 1.02) were the most severe stressors identified by physicians and nurses in both periods. Overall, the use of protective measures (T1: mean 2.66, SD 0.60; T2: mean 2.66, SD 0.60) and acquiring information about COVID-19 (T1: mean 2.29, SD 0.82; T2: mean 1.99, SD 0.89) were identified as the most common coping strategies for the entire study population. Using thematic analysis, we identified 8 themes of personal experiences on the micro, meso, and macro levels. Measures, working conditions, feelings and emotions, and social climate were frequently mentioned topics of the participants. In T1, feelings of isolation and uncertainty were prominent. In T2, feelings of exhaustion were expressed and vaccination was frequently discussed. Moreover, unmet psychosocial needs were identified. CONCLUSIONS: There is a need for improvement in pandemic preparedness. Targeted vocational education measures and setting up of web-based mental health support could be useful to bridge gaps in psychosocial support needs in future crises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".