Edited by: Shan Jiang, Zhejiang University, China
Reviewed by: Mansoor Malik, Johns Hopkins Medicine, United States
Jordi Fernandez-Castro, Autonomous University of Barcelona, Spain
Jonathan Ripp, Mount Sinai Hospital, United States
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
During times of conflict, healthcare personnel face a heightened vulnerability to experiencing psychological problems such as burnout. The impact of conflict or wars on mental health professionals in Palestine and their strategies for managing these problems are currently not recognized. This study sought to assess the prevalence of burnout symptoms and coping strategies among healthcare workers in Palestine, in the context of the ongoing conflict and political violence.
The study utilized a cross-sectional research design. Self-reported questionnaires, including the shortened version (9 items) of The Maslach Burnout Questionnaire and Brief COPE scales, were used to gather data. The relationship between the research variables and burn symptoms was investigated using Chi-squared test, Student’s
A total of 713 participants were recruited. The prevalence rates were as follows: burnout—(72.9%), emotional exhaustion (44.2%), depersonalization (9.8%), and low personal accomplishment (72.2%). The multivariate analysis found that those who worked more than 16 h per day and those who had 1–15 years of job experience were more likely to had emotional exhaustion. Active coping, substance use, informational support, and emotional support all reduced the likelihood of burnout while behavioral disengagement and self-blame increased the possibility of experiencing burnout. Finally, substance use decreased emotional exhaustion, increased depersonalization and enhanced personal accomplishment.
The findings revealed a high prevalence of burnout among health professionals during wartime and political violence. As a result, health professionals are vulnerable to mental problems during political violence and they need immediate assistance in enhancing their mental wellbeing through psychological support, and comprehensive training in stress management.
War is a condition of armed enmity and violence between or within countries (
Burnout is particularly prevalent in demanding professions like healthcare (
The strategies employed by healthcare workers to manage stress might influence their vulnerability to burnout (
In Palestine, the Israeli military has occupied the West Bank and Gaza Strip since 1967. Israel unilaterally withdrew all of its armed forces and settlers from the Gaza Strip in 2005. The Palestinian Ministry of Health (MOH), UNRWA, NGOs, and the private sector are all involved in the administration of healthcare services in Palestine, which also include primary, secondary, and tertiary levels (
This prevailing political instability and recurrent episodes of violence have engendered a dangerous environment for both patients and healthcare professionals. The difficulties in delivering and accessing healthcare services are made even more complex by restricted mobility, limited resources, and persistent security concerns (
The study was a descriptive cross-sectional survey that ran from 15 January 2024 to 1 February 2024. An online sample size calculator
The study used a self-reported questionnaire, which had the following three sections:
Validated burnout thresholds for the a-MBI were employed (>6 for depersonalization, >9 for emotional exhaustion, and < 9 for personal achievement). In accordance with prior research, participants were classified as experiencing burnout if they exceeded the established threshold scores in either the depersonalization or emotional exhaustion domains (
The survey was translated into Arabic and back into English. The Arabic terminology was piloted by 20 health professionals and reviewed by five experts to verify accuracy and understandability.
The data were analyzed using SPSS version 25 (IBM Corp., Chicago, Illinois, USA). A descriptive analysis was performed using the means and the standard deviations for the quantitative variables, and frequencies and percentages for the categorical variables. Chi-squared test, Student’s
The Declaration of Helsinki was followed in the implementation of all study methods. Al Quds University Research Ethical Committee approval was obtained (Ref No: 347/REC/2023). There was anonymity in this online survey. At the outset of the survey, written information was given regarding its goal and the intended use of the data. By completing the questionnaire, the participants gave their informed consent to take part in the research.
The sample included 713 healthcare professionals, 60.3% of whom were female. 53.2% were aged 18–30, 43% were unmarried, and 47.3% earned more than $1,150 every month. Nursing accounted for 57.2% of participants, with 64.7% working in government hospitals and primary care centers. Additionally, 70% had a bachelor’s degree, 46.1% had been working in their present position for more than 6 years, and 64.9% worked an eight-hour shift daily as seen in
Sociodemographic characteristics occupation and work conditions of study participants.
| Frequency (N) | Percent (%) | ||
|---|---|---|---|
| Gender | Male | 283 | 39.7% |
| Female | 430 | 60.3% | |
| Age (years) | 18–30 | 379 | 53.2% |
| 31–40 | 191 | 26.8% | |
| 41+ | 143 | 20.1% | |
| Living place | City | 341 | 47.8% |
| Village | 331 | 46.4% | |
| Refugee camp | 41 | 5.8% | |
| Marital status | Single | 308 | 43.2% |
| Not single* | 405 | 56.8% | |
| Monthly income (US$) | No income | 50 | 7.0% |
| < 570 | 52 | 7.3% | |
| 570–1,150 | 275 | 38.6% | |
| 1,151–1700 | 209 | 29.3% | |
| 1701+ | 127 | 17.8% | |
| Occupation | Allied profession | 134 | 18.8% |
| Physicians | 111 | 15.6% | |
| Pharmacists | 60 | 8.4% | |
| Nurses | 408 | 57.2% | |
| Work place | Governmental | 461 | 64.7% |
| Private | 172 | 24.1% | |
| Others** | 80 | 11.2% | |
| Governate | North | 230 | 32.3% |
| Middle | 221 | 31.0% | |
| South | 262 | 36.7% | |
| Education level | Bachelor | 504 | 70.7% |
| Master | 98 | 13.7% | |
| Doctoral | 23 | 3.2% | |
| Diploma | 88 | 12.3% | |
| Years of experience | < 1 | 147 | 20.6% |
| 1–3 | 120 | 16.8% | |
| 4–6 | 117 | 16.4% | |
| 7–10 | 90 | 12.6% | |
| 11–15 | 112 | 15.7% | |
| 16+ | 127 | 17.8% | |
| Working hours per day | 8 | 463 | 64.9% |
| 9–12 | 212 | 29.7% | |
| 13–16 | 25 | 3.5% | |
| 17+ | 13 | 1.8% | |
*Married/widowed/divorced. ** Non-governmental organizations, civil institutes, and international agencies.
The overall burnout mean was 22.47 (SD 8.65), with a median of 22 (25th percentile = 17, 75th percentile = 28). Thus, 24% had a score less than 17 (
Coping strategies subscale.
| Mean (SD) | Percentile 25 | Median | Percentile 75 | ||
|---|---|---|---|---|---|
| Active coping | 5.49 | (±1.52) | 4.00 | 6.00 | 7.00 |
| Use of informational support | 4.35 | (±1.50) | 3.00 | 4.00 | 5.00 |
| Positive reframing | 4.44 | (±1.72) | 3.00 | 4.00 | 6.00 |
| Planning | 4.95 | (±1.56) | 4.00 | 5.00 | 6.00 |
| Emotional support | 4.05 | (±1.69) | 3.00 | 4.00 | 5.00 |
| Venting | 4.63 | (±1.50) | 4.00 | 5.00 | 6.00 |
| Humor | 2.70 | (±1.29) | 2.00 | 2.00 | 3.00 |
| Acceptance | 5.71 | (±1.60) | 5.00 | 6.00 | 7.00 |
| Religion | 6.45 | (±1.59) | 5.00 | 7.00 | 8.00 |
| Self-blame | 3.80 | (±1.54) | 3.00 | 4.00 | 5.00 |
| Self-distraction | 4.93 | (±1.51) | 4.00 | 5.00 | 6.00 |
| Denial | 4.32 | (±1.80) | 3.00 | 4.00 | 6.00 |
| Substance use | 2.45 | (±1.18) | 2.00 | 2.00 | 2.00 |
| Behavioral disengagement | 3.78 | (±1.50) | 2.00 | 4.00 | 5.00 |
SD, Standard Deviation.
Subgroup analysis was conducted to explore the burnout level in different socio-demographics (
Associations between socio-demographic, occupation, and work condition variables and Burnout symptom level (
| Socio-demographic variables | Emotional exhaustion | Depersonalization | Personal accomplishment | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ≤9 low to moderate |
>9 high |
Chi-squared | ≤6 low to moderate |
>6 high |
chi-squared | >9 low to moderate |
≤9 high |
Chi-squared | ||||||||
| N | % | N | % | N | % | N | % | N | % | N | % | |||||
| Occupation | Allied profession | 82 | 20.6% | 52 | 16.5%* | 0.004 | 122 | 19.0% | 12 | 17.1% | 0.971 | 95 | 18.4% | 39 | 19.7% | 0.366 |
| Physicians | 48 | 12.1% | 63 | 20.0% | 99 | 15.4% | 12 | 17.1% | 87 | 16.9% | 24 | 12.1% | ||||
| Pharmacists | 27 | 6.8% | 33 | 10.5% | 54 | 8.4% | 6 | 8.6% | 40 | 7.8% | 20 | 10.1% | ||||
| Nurses | 241 | 60.6% | 167 | 53.0% | 368 | 57.2% | 40 | 57.1% | 293 | 56.9% | 115 | 58.1% | ||||
| Work place | Government | 252 | 63.3% | 209 | 66.3% | 0.136 | 421 | 65.5% | 40 | 57.1% | 0.048 | 344 | 66.8% | 117 | 59.1%* | 0.026 |
| Private | 93 | 23.4% | 79 | 25.1% | 156 | 24.3% | 16 | 22.9% | 123 | 23.9% | 49 | 24.7% | ||||
| Others | 53 | 13.3% | 27 | 8.6% | 66 | 10.3% | 14 | 20.0% | 48 | 9.3% | 32 | 16.2% | ||||
| Gender | Male | 156 | 39.2% | 127 | 40.3% | 0.761 | 245 | 38.1% | 38 | 54.3% | 0.009* | 191 | 37.1% | 92 | 46.5%* | 0.022 |
| Female | 242 | 60.8% | 188 | 59.7% | 398 | 61.9% | 32 | 45.7% | 324 | 62.9% | 106 | 53.5% | ||||
| Age (years) | 18–30 | 221 | 55.5% | 158 | 50.2%* | 0.005 | 330 | 51.3% | 49 | 70.0% | 0.000* | 253 | 49.1% | 126 | 63.6%* | 0.000 |
| 31–40 | 88 | 22.1% | 103 | 32.7% | 171 | 26.6% | 20 | 28.6% | 139 | 27.0% | 52 | 26.3% | ||||
| 41+ | 89 | 22.4% | 54 | 17.1% | 142 | 22.1% | 1 | 1.4% | 123 | 23.9% | 20 | 10.1% | ||||
| Living | City | 176 | 44.2% | 165 | 52.4% | 0.094 | 302 | 47.0% | 39 | 55.7% | 0.154 | 249 | 48.3% | 92 | 46.5% | 0.744 |
| Village | 197 | 49.5% | 134 | 42.5% | 301 | 46.8% | 30 | 42.9% | 235 | 45.6% | 96 | 48.5% | ||||
| Refugee camp | 25 | 6.3% | 16 | 5.1% | 40 | 6.2% | 1 | 1.4% | 31 | 6.0% | 10 | 5.1% | ||||
| Marital status | Single | 182 | 45.7% | 126 | 40.0% | 0.125 | 266 | 41.4% | 42 | 60.0% | 0.003 | 208 | 40.4% | 100 | 50.5% | 0.015 |
| Not single | 216 | 54.3% | 189 | 60.0% | 377 | 58.6% | 28 | 40.0% | 307 | 59.6% | 98 | 49.5% | ||||
| Monthly Income (US $) | No income | 37 | 9.3% | 13 | 4.1%* | 0.023 | 43 | 6.7% | 7 | 10.0% | 0.035 | 31 | 6.0% | 19 | 9.6%* | 0.030 |
| < 570 | 35 | 8.8% | 17 | 5.4% | 49 | 7.6% | 3 | 4.3% | 37 | 7.2% | 15 | 7.6% | ||||
| 570–1,150 | 144 | 36.2% | 131 | 41.6% | 239 | 37.2% | 36 | 51.4% | 186 | 36.1% | 89 | 44.9% | ||||
| 1,150–1700 | 115 | 28.9% | 94 | 29.8% | 190 | 29.5% | 19 | 27.1% | 164 | 31.8% | 45 | 22.7% | ||||
| 1701+ | 67 | 16.8% | 60 | 19.0% | 122 | 19.0% | 5 | 7.1% | 97 | 18.8% | 30 | 15.2% | ||||
| Area of work | North | 119 | 29.9% | 111 | 35.2% | 0.128 | 212 | 33.0% | 18 | 25.7% | 0.436 | 175 | 34.0% | 55 | 27.8% | 0.248 |
| Middle | 135 | 33.9% | 86 | 27.3% | 196 | 30.5% | 25 | 35.7% | 153 | 29.7% | 68 | 34.3% | ||||
| South | 144 | 36.2% | 118 | 37.5% | 235 | 36.5% | 27 | 38.6% | 187 | 36.3% | 75 | 37.9% | ||||
| Degree level | Bachelor | 280 | 70.4% | 224 | 71.1%* | 0.002 | 448 | 69.7% | 56 | 80.0% | 0.074 | 344 | 66.8% | 160 | 80.8%* | 0.003 |
| Master | 46 | 11.6% | 52 | 16.5% | 89 | 13.8% | 9 | 12.9% | 81 | 15.7% | 17 | 8.6% | ||||
| Doctoral | 9 | 2.3% | 14 | 4.4% | 20 | 3.1% | 3 | 4.3% | 20 | 3.9% | 3 | 1.5% | ||||
| Diploma | 63 | 15.8% | 25 | 7.9% | 86 | 13.4% | 2 | 2.9% | 70 | 13.6% | 18 | 9.1% | ||||
| Years of experience | < 1 | 106 | 26.6% | 41 | 13.0%* | 0.000 | 126 | 19.6% | 21 | 30.0% | 0.001 | 93 | 18.1% | 54 | 27.3%* | 0.000 |
| 1–3 | 59 | 14.8% | 61 | 19.4% | 108 | 16.8% | 12 | 17.1% | 84 | 16.3% | 36 | 18.2% | ||||
| 4–6 | 61 | 15.3% | 56 | 17.8% | 101 | 15.7% | 16 | 22.9% | 84 | 16.3% | 33 | 16.7% | ||||
| 7–10 | 35 | 8.8% | 55 | 17.5% | 77 | 12.0% | 13 | 18.6% | 55 | 10.7% | 35 | 17.7% | ||||
| 11–15 | 56 | 14.1% | 56 | 17.8% | 105 | 16.3% | 7 | 10.0% | 91 | 17.7% | 21 | 10.6% | ||||
| 16+ | 81 | 20.4% | 46 | 14.6% | 126 | 19.6% | 1 | 1.4% | 108 | 21.0% | 19 | 9.6% | ||||
| Working hours per day | 8 | 280 | 70.4% | 183 | 58.1%* | 0.002 | 427 | 66.4% | 36 | 51.4% | 0.009 | 334 | 64.9% | 129 | 65.2% | 0.517 |
| 9–12 | 103 | 25.9% | 109 | 34.6% | 184 | 28.6% | 28 | 40.0% | 157 | 30.5% | 55 | 27.8% | ||||
| 13–16 | 12 | 3.0% | 13 | 4.1% | 23 | 3.6% | 2 | 2.9% | 15 | 2.9% | 10 | 5.1% | ||||
| 17+ | 3 | 0.8% | 10 | 3.2% | 9 | 1.4% | 4 | 5.7% | 9 | 1.7% | 4 | 2.0% | ||||
Subgroup analysis was conducted to explore the burnout level in different coping strategies (
Association between brief coping and Burnout symptom level (
| Emotional exhaustion | Depersonalization | Personal accomplishment | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ≤9 low to moderate |
>9 high |
T-test | ≤6 low to moderate |
>6 high |
T-test | >9 low to moderate |
≤9 high |
||||||||
| Mean | SD | Mean | SD | Sig. | Mean | SD | Mean | SD | Sig. | Mean | SD | Mean | SD | Sig. | |
| Active coping | 5.62 | 1.51 | 5.33 | 1.53 | 5.54 | 1.54 | 5.07 | 1.29 | 5.60 | 1.51 | 5.21 | 1.51 | |||
| Use of informational support | 4.29 | 1.45 | 4.42 | 1.56 | 0.323 | 4.31 | 1.50 | 4.70 | 1.46 | 4.34 | 1.47 | 4.36 | 1.58 | 0.920 | |
| Positive reframing | 4.50 | 1.69 | 4.36 | 1.76 | 0.267 | 4.40 | 1.74 | 4.73 | 1.52 | 0.135 | 4.36 | 1.74 | 4.65 | 1.65 | |
| Planning, items | 4.96 | 1.57 | 4.94 | 1.54 | 0.845 | 4.95 | 1.57 | 4.94 | 1.44 | 0.976 | 4.99 | 1.57 | 4.83 | 1.53 | 0.259 |
| Emotional support | 4.17 | 1.73 | 3.90 | 1.63 | 4.02 | 1.71 | 4.29 | 1.42 | 0.212 | 3.99 | 1.68 | 4.20 | 1.71 | 0.113 | |
| Venting | 4.53 | 1.53 | 4.76 | 1.45 | 4.59 | 1.51 | 4.93 | 1.37 | 0.076 | 4.67 | 1.49 | 4.53 | 1.51 | 0.236 | |
| Humor | 2.69 | 1.33 | 2.71 | 1.24 | 0.358* | 2.61 | 1.21 | 3.47 | 1.68 | 2.56 | 1.10 | 3.07 | 1.62 | ||
| Acceptance | 5.73 | 1.58 | 5.67 | 1.61 | 0.619 | 5.73 | 1.61 | 5.44 | 1.43 | 0.147 | 5.78 | 1.63 | 5.52 | 1.50 | |
| Religion | 6.52 | 1.56 | 6.38 | 1.62 | 0.275 | 6.51 | 1.56 | 5.94 | 1.70 | 6.58 | 1.53 | 6.13 | 1.67 | ||
| Self-blame | 3.68 | 1.53 | 3.96 | 1.55 | 3.70 | 1.50 | 4.73 | 1.62 | 3.70 | 1.51 | 4.06 | 1.60 | |||
| Self-distraction | 4.98 | 1.52 | 4.85 | 1.50 | 0.353 | 4.91 | 1.53 | 5.03 | 1.38 | 0.550 | 4.88 | 1.50 | 5.06 | 1.56 | 0.147 |
| Denial | 4.21 | 1.81 | 4.45 | 1.78 | 0.053 | 4.26 | 1.80 | 4.80 | 1.72 | 4.24 | 1.81 | 4.52 | 1.75 | ||
| Substance use | 2.51 | 1.23 | 2.38 | 1.12 | 0.054* | 2.37 | 1.07 | 3.24 | 1.74 | 2.28 | 0.94 | 2.91 | 1.57 | ||
| Behavioral disengagement | 3.63 | 1.49 | 3.98 | 1.49 | 3.71 | 1.49 | 4.46 | 1.38 | 3.70 | 1.47 | 4.01 | 1.55 | |||
SD, Standard deviation; Sig., Significance. *Mann–Whitney test significance.
Multivariate analysis supported some of the previously stated findings. The results revealed that increasing participants’ years of experience, working hours per day, and usage of behavioral disengagement increased the likelihood of emotional exhaustion (AOR = 1.26, 95% CI = 1.21–1.461). Additionally, active coping, substance use, informational support, and emotional support reduced the likelihood of experiencing emotional exhaustion. Furthermore, active coping reduced the probability of experiencing depersonalization (AOR = 0.745, 95% CI = 0.656–0.938), but substance use and self-blame increased the likelihood of having it. Furthermore, increasing the number of experience years and using active coping reduced the likelihood of having personal accomplishment (AOR = 0.846, 95%CI = 0.755–0.948), whereas substance use increased the odds of having it (AOR = 1.457, 95%CI = 1.271–1.670) (
Multivariate regression analysis for determinants of Burnout symptom.
| Emotional exhaustion | Depersonalization | Personal accomplishment | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sig. | AOR | 95% CI AOR | 95% CI AOR | 95% CI AOR | ||||||||
| Lower | Upper | Sig. | AOR | Lower | Upper | Sig. | AOR | Lower | Upper | |||
| Age (years) | 0.028 | |||||||||||
| Ref. | ||||||||||||
| 0.671 | 0.883 | 0.496 | 1.570 | |||||||||
| 0.066 | 0.009 | 0.487 | ||||||||||
| Years of work | ||||||||||||
| > 1 year | Ref. | |||||||||||
| 1–3 years | 2.279 | 1.343 | 3.867 | 0.546 | 0.848 | 0.495 | 1.450 | |||||
| 4–6 years | 2.033 | 1.189 | 3.476 | 0.282 | 0.742 | 0.430 | 1.278 | |||||
| 7–10 years | 3.476 | 1.938 | 6.234 | 0.335 | 1.320 | 0.751 | 2.319 | |||||
| 11–15 years | 2.344 | 1.363 | 4.030 | 0.475 | 0.261 | 0.868 | ||||||
| 16+ years | 0.286 | 1.339 | 0.783 | 2.288 | 0.390 | 0.212 | 0.719 | |||||
| Hours you work per day | ||||||||||||
| 8 h | Ref. | |||||||||||
| 9–12 h | 0.087 | 1.359 | 0.957 | 1.931 | ||||||||
| 13–16 h | 0.454 | 1.381 | 0.593 | 3.216 | ||||||||
| 17+ hours | 4.923 | 1.249 | 19.406 | |||||||||
| Coping strategies | ||||||||||||
| Active coping | 0.875 | 0.785 | 0.975 | 0.784 | 0.656 | 0.938 | 0.846 | 0.755 | 0.948 | |||
| Substance use | 0.830 | 0.713 | 0.966 | 1.276 | 1.077 | 1.512 | 1.457 | 1.271 | 1.670 | |||
| Self Balme | 1.361 | 1.141 | 1.624 | |||||||||
| Use of informational support | 1.225 | 1.067 | 1.406 | |||||||||
| Emotional support | 0.815 | 0.718 | 0.925 | |||||||||
| Behavioral disengagement | 1.260 | 1.121 | 1.416 | |||||||||
AOR, Adjusted Odds Ratio; 95% CI, 95% confidence interval. Values in bold are significant.
The mental health of health professionals is a critical problem because they are in charge of delivering long-term care to the people. The current study found that more than two-thirds of Palestinian health professionals (72.9%) experienced burnout, 44.2% had emotional exhaustion, 9.8% had depersonalization, and 72.2% experienced low personal accomplishment. Burnout findings are considered high when compared to other research in the literature review. Hamdan and Hamra (
Other research, on the other hand, found high level of burnout similar to the current study. According to one systematic review, the prevalence of high burnout varied from 0.9 to 40%, high emotional exhaustion from 0 to 49.7%, depersonalization from 0 to 59.6%, and low personal accomplishment from 0 to 60% (
The findings of the current study revealed several factors that may lead to an increase in burnout among Palestinian health professionals. The study found that those who worked more than 16 h per day and health professionals with 7–10 years of work experience were at the greatest risk of developing emotional exhaustion, followed by those with 1–3 years of experience. Also, those with 4–6 years of experience were at a higher risk of emotional exhaustion than health professionals with less than 1 year of experience.
These findings are supported by other studies in the literature review (
On the other hand, our study found that professionals with more than a decade of work experience were more likely to have personal accomplishments and less likely to experience burnout than those with less than a year of experience. Work experience can be linked to workers’ capacity and autonomy in shaping their responsibilities positively. Employees who deliberately created a productive and stimulating work environment for themselves reported an increase in psychological resources such as hope, resilience, self-efficacy, and optimism, as well as improved job satisfaction (
Furthermore, the current study found that Palestinian health professionals used several coping strategies to minimize burnout. Active coping, substance use, informational support, and emotional support all decreased the probability of experiencing emotional exhaustion. Furthermore, active coping decreased the likelihood of developing depersonalization, but substance use increased personal accomplishment. Other research revealed similar findings. Menaldi et al. found that active coping, instrumental support, and substance use reduced the likelihood of experiencing EE (
Interestingly, our research indicated that substance use decreased EE and enhanced personal accomplishment. Avoidant coping strategies, such as substance use, are not necessarily maladaptive and can have different impacts depending on the level of stress (
Furthermore, the current study’s findings revealed that behavioral disengagement increased the probability of experiencing emotional exhaustion, whereas self-blame raised the likelihood of depersonalization. Other research found similar findings (
This research has limitations. Convenience sampling and cross-sectional designs reduce the ability to demonstrate causal relationships and affect the representativeness of the sample and the generalizability of the findings. Furthermore, there is a possibility of reporting bias due to the use of a self-reported questionnaire. Given that recruiting took place using platforms such as Google Docs and WhatsApp, it is probable that health professionals already employed in the impacted areas, including the Gaza Strip, do not have access to or the chance to use this technology. As a result, this situation may have an impact on the sample’s representativeness. Additionally, few studies examine burnout among healthcare professionals during wartime. As a result, comparing our findings to other studies is limited. Also, in this study we were not able to calculate the response rate due to the use of anonymous online questionnaire. Despite these limitations, the findings of the current study provide light on the psychological well-being of health workers living in conflict-affected countries. This study provides a major contribution to the current literature since it is the first to analyze burnout and coping methods among Palestinian healthcare professionals throughout periods of armed conflict and political violence.
As a result, professionals must develop effective coping strategies early in their careers to reduce the risk of developing burn out. Training sessions or seminars can help health practitioners understand their typical defense style and how it affects them and their ability to provide treatment during political violence. Furthermore, excessive workloads, prolonged working hours, a stressful work environment, and substandard workplace conditions have been identified as risk factors for stress, maladaptation, and burnout among health professionals. Consequently, health policymakers and managers can enhance stress adaptation among these health professionals during wartime by improving sleep quality and hours, offering a nutritious diet, reducing excessive workloads, promoting regular physical exercise and relaxation techniques, facilitating social engagement, ensuring a suitable work-life balance, and guaranteeing the physical safety of their employees (
More qualitative and quantitative research is required to fully understand the factors that increase burn out among health professionals and coping strategies for dealing with patients during political violence. Additional study is required to examine which drugs Palestinian health professionals use to deal with the stress of political violence. Finally, further research is needed to assess burnout levels in certain health professions in order to investigate burnout levels and related causes during wars and political violence.
The study found that burnout symptoms are common among Palestinian health professionals, particularly in less experienced professionals and those who work for long ours. In addition, using self-blame, and behavioral disengagement as coping mechanisms increases the likelihood of developing burnout symptoms. The study highlights the significance of identifying stressors faced by health professionals during wartime and implementing measures to prevent occupational burnout. Consequently, health professionals need immediate assistance in enhancing their mental wellbeing through psychological intervention and, and comprehensive training in stress management.
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
The studies involving humans were approved by Al Quds University Research Ethical Committee (Ref No: 347/REC/2023). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. The social media data was accessed and analyzed in accordance with the platform’s terms of use and all relevant institutional/national regulations.
MA: Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing. NE: Data curation, Formal analysis, Writing – original draft, Writing – review & editing. AA: Data curation, Validation, Writing – original draft, Writing – review & editing. AH: Data curation, Validation, Writing – review & editing. MZ: Data curation, Validation, Writing – review & editing.
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
The authors would like to thank all participants for their participation in the study.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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