Understanding the Mental and Physical Burdens of Physicians and Identifying Support Interventions in Bangladesh: Qualitative Study
Bibliographic record
Abstract
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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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".