Challenges, experience and coping of health professionals in delivering healthcare in an urban slum in India during the first 40 days of COVID-19 crisis: a mixed method study

George, Carolin Elizabeth; Inbaraj, Leeberk Raja; Rajukutty, Shon; Witte, L.P. de

doi:10.1136/bmjopen-2020-042171

Cited by 65 publications

(137 citation statements)

References 31 publications

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“… 30 , 31 The reported prevalence of burnout and psychological problems among HCW in Indian studies ranges from more than a third to a half of all studied. 9 , 10 , 11 , 12 , 13 Survey being done in the later phase of pandemic as compared to published reports, use of a more validated instrument for burnout with clearer definitions and inclusion of a larger diverse sample size are the likely explanations for the lower rates of burnout. Possibly, the burnout and anxiety were much more in the early phase of pandemic even though the COVID-19 cases were far less in India.…”

Section: Discussionmentioning

confidence: 99%

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COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study

Gupta

Jha

Bansal

et al. 2021

Indian Heart Journal

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Objectives COVID-19 pandemic has led to unprecedented increase in rates of stress and burn out among healthcare workers (HCWs). Heart rate variability (HRV) has been shown to be reflective of stress and burnout. The present study evaluated the prevalence of burnout and attempted to develop a HRV based predictive machine learning (ML) model to detect burnout among HCWs during COVID-19 pandemic. Methods Mini-Z 1.0 survey was collected from 1615 HCWs, of whom 664, 512 and 439 were frontline, second-line and non-COVID HCWs respectively. Burnout was defined as score ≥ 3 on Mini-Z-burnout-item. A 12-lead digitized ECG recording was performed and ECG features of HRV were obtained using feature extraction. A ML model comprising demographic and HRV features was developed to detect burnout. Results Burnout rates were higher among second-line workers 20.5% than frontline 14.9% and non-COVID 13.2% workers. In multivariable analyses, features associated with higher likelihood of burnout were feeling stressed (OR = 6.02), feeling dissatisfied with current job (OR = 5.15), working in a chaotic, hectic environment (OR = 2.09) and feeling that COVID has significantly impacted the mental wellbeing (OR = 6.02). HCWs with burnout had a significantly lower HRV parameters like root mean square of successive RR intervals differences (RMSSD) [P<0.0001] and standard deviation of the time interval between successive RR intervals (SDNN) [P<0.001]) as compared to normal subjects. Extra tree classifier was the best performing ML model (sensitivity: 84%) Conclusion In this study of HCWs from India, burnout prevalence was lower than reports from developed nations, and was higher among second-line versus frontline workers. Incorporation of HRV based ML model predicted burnout among HCWs with a good accuracy.

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Section: Discussionmentioning

confidence: 99%

“…This is especially important given that India has experienced the second highest burden of cumulative cases in the world. The prior studies had either small sample sizes 9 , 10 , 11 , 12 or included only a uniform group of participants. 13 Hence, the generalizability of these studies is limited.…”

Section: Introductionmentioning

confidence: 99%

COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study

Gupta

Jha

Bansal

et al. 2021

Indian Heart Journal

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“…And the communities at large need to behave responsibly towards HCS to discharge their duties efficiently. The supportive, collaborative working atmosphere with trust and professional unity can help HCS discharge their COVID‐19 duties more effectively (George et al, 2020 ). Indeed, to boost the morale of HCS and take their active support during the COVID‐19 fight, policymakers and managers need to give attention to appropriate insurance/monetary incentives to HCS.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, continuous communication, dedicated psychological support, and sensible and straightforward public education campaigns are paramount (Chandra & Vanjare, 2020 ; Ramaci et al, 2020 ). The healthcare administration and government should make sure that the culture of resilience is embraced in HCI as a part of health delivery, helping fight present and future pandemics (George et al, 2020 ).…”

Section: Conclusion and Future Scopementioning

confidence: 99%

COVID‐19 critical success factors in Indian healthcare industry—A DEMATEL approach

Gedam

Raut

Inamdar

et al. 2021

Multi Criteria Decision Anal

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The prosed study aims to provide COVID‐19 critical success factors (CSF) associated with pandemic circumstances in the Indian healthcare industry (HCI). The CSF was identified via expert team inputs and a detailed literature review. The Decision‐Making Trial and Evaluation Laboratory (DEMATEL) method is used to determine the causal relationship between identified CSF. The methodology was supported by the case study of the Indian HCI. A total of 15 CSF in the Indian HCI during COVID‐19 are identified and prioritized using the DEMATEL method. The findings indicate that the high‐quality personal protective equipment (PPEs; LC8) and testing laboratories/facilities, centres, and kits (LC15) are the significant cause, and appropriate healthcare laws (LC13) are the least effect group. The study shows that policy and decision‐makers need to emphasize on LC8 and LC15 CSF in the Indian HCI and act accordingly to win the battle against post‐COVID‐19 circumstance. The policy/decision‐makers and healthcare administrations can identify the CSF and focus on that particular CSF. The identified CSF will help policy and decision‐makers swiftly build up the HCI to cope with the future pandemic.

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“…Suddenly they were overburdened with work and extreme exposure to aversive circumstances. [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Managing Stress among Healthcare Workers: Strategies and Implications Associated With Covid-19 Pandemic

Rajkumar¹,

Kumar²,

Sudha³

2021

IRJASH

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Corona virus disease or Covid-19 has become a global pandemic. It was first reported in Wuhan, china. The disease has crossed all borders across the world and today the whole world is suffering from the pandemic now. So when Covid-19 started shaking the world with immense spreading of its first wave and second wave, the whole population is completely dependent on the healthcare sector. Since the healthcare workers are part of the service sector, they had to support and take care of the patients by risking their lives. The stress taken by the healthcare workers is beyond the imagination of a layman and is greater than normal work stress. Covid-19 made its entry abruptly, putting a high level of stress on the medical community. There has never has been such a catastrophic pandemic that has occurred in last five decades. The hospital infrastructure was never ready for such a huge pandemic and its implications. More patient load, high infectivity rate, increase in mortality rate, family getting infected, more serious infections, scarcity of medicines, anxiety of getting infected etc. all contributed to the increase in stress level of the healthcare workers. This study aims to understand the stress faced by the healthcare workers during the first and second wave of the pandemic and identifying the solutions to reduce stress and promote their mental health by listening to good music, having a good sleep, opening up to a counsellor, getting spiritual help etc., if a third wave occurs.

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Challenges, experience and coping of health professionals in delivering healthcare in an urban slum in India during the first 40 days of COVID-19 crisis: a mixed method study

Cited by 65 publications

References 31 publications

COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study

COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study

COVID‐19 critical success factors in Indian healthcare industry—A DEMATEL approach

Managing Stress among Healthcare Workers: Strategies and Implications Associated With Covid-19 Pandemic

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