Advancing Mental Health and Psychological Support for Health Care Workers Using Digital Technologies and Platforms
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic is a global public health crisis that has not only endangered the lives of patients but also resulted in increased psychological issues among medical professionals, especially frontline health care workers. As the crisis caused by the pandemic shifts from acute to protracted, attention should be paid to the devastating impacts on health care workers' mental health and social well-being. Digital technologies are being harnessed to support the responses to the pandemic, which provide opportunities to advance mental health and psychological support for health care workers. OBJECTIVE: The aim of this study is to develop a framework to describe and organize the psychological and mental health issues that health care workers are facing during the COVID-19 pandemic. Based on the framework, this study also proposes interventions from digital health perspectives that health care workers can leverage during and after the pandemic. METHODS: The psychological problems and mental health issues that health care workers have encountered during the COVID-19 pandemic were reviewed and analyzed based on the proposed MEET (Mental Health, Environment, Event, and Technology) framework, which also demonstrated the interactions among mental health, digital interventions, and social support. RESULTS: Health care workers are facing increased risk of experiencing mental health issues due to the COVID-19 pandemic, including burnout, fear, worry, distress, pressure, anxiety, and depression. These negative emotional stressors may cause psychological problems for health care workers and affect their physical and mental health. Digital technologies and platforms are playing pivotal roles in mitigating psychological issues and providing effective support. The proposed framework enabled a better understanding of how to mitigate the psychological effects during the pandemic, recover from associated experiences, and provide comprehensive institutional and societal infrastructures for the well-being of health care workers. CONCLUSIONS: The COVID-19 pandemic presents unprecedented challenges due to its prolonged uncertainty, immediate threat to patient safety, and evolving professional demands. It is urgent to protect the mental health and strengthen the psychological resilience of health care workers. Given that the pandemic is expected to exist for a long time, caring for mental health has become a "new normal" that needs a strengthened multisector collaboration to facilitate support and reduce health disparities. The proposed MEET framework could provide structured guidelines for further studies on how technology interacts with mental and psychological health for different populations.
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 | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».