Unmet Psychosocial Needs of Health Care Professionals in Europe During the COVID-19 Pandemic: Mixed Methods Approach
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».