Understanding the Mental and Physical Burdens of Physicians and Identifying Support Interventions in Bangladesh: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic had a substantial, negative impact on the world, and physicians played a crucial role in providing health care while facing the risk of contracting the virus themselves. While working on the frontlines, they also needed to protect themselves and their families from the virus. Unfortunately, their mental health was not given the attention it deserved. Many physicians experienced burnout due to the numerous challenges they faced, yet they received little support. Resource-limited countries such as Bangladesh were particularly affected due to a lack of resources. Although high-income countries have proposed a well-being model for physicians, this model is not directly applicable to resource-limited nations. However, redefining the model to suit the specific needs of physicians in resource-limited countries could provide sustainable support for their well-being. OBJECTIVE: We aimed to gain a deeper understanding of the mental and physical burdens faced by Bangladeshi physicians during the COVID-19 pandemic, and the contextual factors influencing their well-being. By understanding these aspects, we can recommend an adaptable, effective, and sustainable contextual model. METHODS: We conducted semistructured online interviews with 14 physicians in Chattogram, Bangladesh, during the COVID-19 pandemic. The physicians actively working in the COVID-19 unit were recruited from public and private hospitals through purposive sampling. Participants were aged between 25 and 35 years and had up to 8 years of working experience, including 43% (6/14) interns, 36% (5/14) medical officers, 14% (2/14) researchers, and 7% (1/14) surgeons. Each interview was conducted in Bengali, and we obtained consent to record the audio. Overall, 637 minutes of discussion were translated and transcribed. The results were analyzed using reflexive thematic analysis. RESULTS: We identified factors that impacted physicians' mental and physical health and well-being during the COVID-19 pandemic. They frequently dealt with undiagnosed patients, which put them at risk. Physicians often feared the potential danger their profession posed to their families, choosing to prioritize their family's safety over their own. In addition, heavy workloads, excessive duty hours, and a shortage of colleagues substantially affected their sleep patterns and disrupted their regular work schedules. Instead of receiving societal support, they often faced negative perceptions from the public. In addition, during times of mass patient deaths, many physicians struggled to cope with their emotions without any mental health support. CONCLUSIONS: Our work shows physicians' mental and physical health burdens with various contextual difficulties. We understood these concerns and suggested a contextual (emphasizes understanding and addressing users' behavior within its specific context) intervention model inspired by the well-being framework. We emphasize the importance of integrating both contextual and technological interventions. Through this model, our goal is to involve stakeholders in redesigning the work environment for physicians, ensuring it is sustainable in the long term and adaptable to different situations.
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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 | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».