Emotional intelligence and tertiary care nurses of Bangalore, India – A cross-sectional study
Minitta Maria Regy, Naveen Ramesh
Department of Community Health, St John's Medical College, Bengaluru, Karnataka, India
|Date of Submission||24-Sep-2022|
|Date of Acceptance||04-Nov-2022|
|Date of Web Publication||28-Apr-2023|
Dr. Naveen Ramesh
Department of Community Health, St John's Medical College, Bengaluru - 560 034, Karnataka
Source of Support: None, Conflict of Interest: None
BACKGROUND: Nursing profession requires diverse skills, and emotional intelligence (EI) plays a role in helping them adapt to adverse situations as a part of their work environment. The study objective was to determine the prevalence of EI with its associated factors among the nursing professionals from selected four tertiary care hospitals in Bangalore.
MATERIALS AND METHODS: This was a multicentric, cross-sectional study done among nurses with more than 1 year of work experience, who were randomly selected from tertiary care hospitals in Bangalore. Data was collected, both online and offline, owing to the ongoing COVID-19 pandemic, and the Emotional Intelligence Scale was used following obtaining informed consent. Data analysis included mean, associations, and regression.
RESULTS: Out of the total 294, the mean age of the study participants was 27 ± 4.92 years. A total of 75 (25.5%) had poor EI. Although there were not any significant association between the specialty and EI subscales, a significant association was found to be present between total years of work experience and all five subscales of EI: self-awareness (P = 0.009), social regulation (P = 0.004), motivation (P = 0.012), social awareness (P = 0.008), and social skills (P = 0.049), respectively. Logistic regression showed a significant finding where nursing staff with more work experience had a higher EI (OR 0.012, 95% CI 1.288–8.075) than those with less work experience.
CONCLUSION: The prevalence of poor EI among nursing professionals was 25%, and EI scores increased with increasing work experience, and this was found to be significant. Thereby, EI building workshops/training, as a part of the nursing curriculum, may help improve their quality of care and resilience in demanding work environments.
Keywords: Emotional intelligence, nurses, tertiary care hospitals
|How to cite this article:|
Regy MM, Ramesh N. Emotional intelligence and tertiary care nurses of Bangalore, India – A cross-sectional study. J Edu Health Promot 2023;12:145
|How to cite this URL:|
Regy MM, Ramesh N. Emotional intelligence and tertiary care nurses of Bangalore, India – A cross-sectional study. J Edu Health Promot [serial online] 2023 [cited 2023 May 30];12:145. Available from: https://www.jehp.net//text.asp?2023/12/1/145/374764
| Introduction|| |
Peter Salovey and John Mayer are recognized as pioneers in emotional intelligence (EI) and have done extensive studies on the same. They proposed a formal definition of EI as “the ability to monitor one's own and others' feelings, and to use this information to guide one's thinking and action,” and later this definition was modified by the same two psychologists where they broke it down into four proposed abilities that are distinct yet related which include: perceiving, using, understanding, and managing emotions.
The conceptualization model of EI includes appraisal and expression of emotions, regulation of emotion and utilization of emotions with sub branches like perceptions, flexible planning, creative thinking, redirected attention, and motivation. Much of the existing literature on EI is based on the Ability Model, which includes correctly identifying emotions in others, using these emotions to reason better, understanding emotions and managing them appropriately.
EI balances various skills that improve one's abilities to evolve, develop, and engage better in management, where it serves as a predictor of social success and adjustment, shaping personal as well professional success better than general intelligence. Another thought from previous literature suggested that there is also a relationship between EI and critical thinking, where critical thinking works as an effective predictor of EI. Especially in demanding work fronts, such as the healthcare sector, this could impact to a great extent.
”Nurse-Patient Interaction” is the core momentum of the nursing practice, and it is a complex process that involves the perception and comprehension of patient emotions and utilization of the same to manage patient situations with the aim of effective patient care.
Despite the ongoing COVID-19 pandemic, just like most healthcare professionals, nurses also suppressed their various difficulties, used maladaptive coping mechanisms, and performed beyond their capacity due to reasons demanding the same that predominantly included socioeconomic challenges and work pressure. Nearly all work-related physical, psychological, and social stressors increased among nurses during the ongoing COVID-19 pandemic and have only added an extra load on their already stressful work front.
The concern of resilience and adaptation to the pressing work environments in changing times cause an indeed great deal of apprehension and pressure, which may reflect in the work output as well as the personal well-being of the staff and personnel; however, there are only a few studies in India which assess EI among healthcare professionals and its associated factors., Therefore, we framed our study objective based on the same as to determine the prevalence of EI and measure its associated factors among the nursing professionals from selected tertiary care hospitals in Bangalore.
| Materials and Methods|| |
Study design and setting
This was a cross-sectional, multicentric study done among nursing professionals in four tertiary care hospitals of Bangalore, taken as institutes A, B, Cs and D with respect to our study. Data was collected from September 2020 to March 2021 using both online and offline methods.
Study participant and sampling
Licensed practicing nurses with a minimum work experience of 1 year were included in the study following a random sampling. The exclusion criteria were nursing staff with serious illness or difficulty in comprehension, nursing staff who were pregnant, and nursing staff who have been already diagnosed with mental health conditions or are currently on treatment for the same.
The sample size was calculated using the formula:
σ – Standard deviation (5.83) [study done in Uttarakhand, India].
d – Precision (0.7).
z (1−α/2) – Two-sided z-value for corresponding α (1.96).
The calculated sample size was 270.
However, due to the pandemic and expecting high attrition, the questionnaire was circulated to a total of 765 staff nurses; ultimately, a total of 294 consented and completed the on-line questionnaire.
Data collection and tool
The study tool consisted of three sections: informed consent, sociodemographic profile, work experience, place of birth, highest education, and the Emotional Intelligence Scale by Dr Shailendra Singh, which comprises 60 questions in total, scored on a Likert scale, assessed by five subcomponents: self-awareness, self-regulation, motivation, social awareness, and social skills, where the individual components were categorized based on severity and associated factors were studied. Permission was obtained from the author for using the tool in this study.
Prior to our study initiation, a pilot study was conducted in August 2020 among nursing professionals with less than 1 year of work experience from two of the tertiary care hospitals included in the study. This was done to check for the feasibility of online data collection process, which we intended in our study as well as for the validation of our study tools. A total of 30 forms were circulated using both online and offline approaches, and a total of 26 forms were attempted to be filled in total, out of which 13 forms were completely filled and 13 were partially filled. There were no nonconsented entries. Data entry were facilitated using RedCap (Research Electronic Data Capture), a web-based application that helps manage research surveys through online medium along with its own database for reference. Time taken for each entry was noted to be around 10–15 min per person. The preliminary analysis was given by RedCap software, and further data cleaning and secondary analysis were done subsequently using Microsoft excel and IBM Statistical Package for Social Sciences (SPSS) software v 21.0.
The institutional ethics committee approval was obtained prior to the study initiation (IEC 302/2019). This was followed by seeking permission from the Nursing Superintendent of the concerned hospitals. The list of nursing professionals with a minimum of 1-year work experience was collected and the nurses fulfilling the inclusion criteria were recruited.
Post the piloting, the final data collection was also done using used RedCap version 9.9.0 and the downloaded data was in Microsoft Excel CSV format following which it was analyzed using IBM SPSS statistical software version 21.0. The normality of the data distribution was checked using the Kolmogorov–Smirnov test and was further analyzed using descriptive statistics like percentages, mean, and standard deviation. The various sociodemographic factors and their association with EI were studied using relevant tests of significance such as Chi-square test and Fisher's exact test followed by tests for regression.
| Results|| |
A total of 765 study questionnaires were distributed; of them, 294 (38.43) questionnaires were completely answered along with the consent and the same was considered for further analysis. There were a total of 45 (15.3%), 127 (43.2%), 32 (10.9%), and 90 (30.6%) from Hospitals A, B, C, and D, respectively.
Half, 147 (50%), of the study participants had 1–2 years of work experience, and one-third of them were involved in surgical 110 (37.4%) specialty [Table 1].
The cut-off scores for each component under EI (self-awareness, self-regulation, motivation, social awareness, and social skills) were calculated and categorized as poor/moderate/good/excellent with each having a minimum score of 12 and maximum score of 60. Fairly the number of subjects distributed between grades was not too extreme from each other under the respective components, where poor (n = 75), moderate (n = 73), good (n = 74), and excellent (n = 72) were noted.
The significant findings found between the socio-demodemographic variable and EI components of the study are mentioned under [Table 2], [Table 2]a and [Table 2]b further analysis by logistic regression are tabulated under [Table 3], [Table 3]a and [Table 3]b. For further analysis, we looked among those associations with P < 0.2 and entered into the binary logistic model combining poor EI and moderate EI as “Low EI,” and good EI and excellent EI as “High EI.”
| Discussion|| |
The Goleman model explains the concept of EI as a constellation of different abilities that can be divided into two major domains for clarity of comprehension – the first part domain largely consists of three dimensions: self-awareness, self-regulation, motivation, and the second domain, which is more important in the context of our goals, and tasks includes: empathy and social skills which assesses both the affective and cognitive skills of the individual.
A study done by Saeed et al. (2011) assessed and compared the EI of nurses in general and intensive care units and the highest frequency of EI among the nurses working in general units was observed to be at a good level (46.1%), whereas the same among the nurses working in intensive care units was observed to be in the level where they require assistance (47.2%), and this difference between the general and intensive care unit staff was statistically significant Another comparative study done by Al-Hamdan et al. (2016), variables under EI studied included – recognizing and expressing emotions, understanding other's emotions, decision making, managing emotions, and controlling emotions, which were found to individually contribute to the outcome of job performance with significant association seen among those nurses working in the medical-surgical wards and effect on job performance and the EI variables of recognizing and expressing emotions and controlling emotions to also have an effect on the same. Results also demonstrated a small, positive correlation between four EI subscales and job performance; however, the relationship between the fifth subscale (controlling emotions) and the total job performance scale was not statistically significant. In our study, although there were not any significant association with the specialty and EI subscales, a significant association was found to be present between total years of work experience and all five subscales of EI: self-awareness, social regulation, motivation, social awareness, and social skills, respectively. This shows that with adequate gain in work experience, resilience, and personal skills improved at both social and work aspects.
In a study done by Chelsea Marvos et al. (2015) which looked into the EI and clinical performance of nurses, a significant positive correlation was found between the EI subscale of understanding emotions and strategic EI, which interprets as those study participants who scored higher on the variable of understanding emotions and strategic EI were at an anticipated longer sustainability in terms of the professional front. In our study, a significant association was found to be present between the variable of age and EI component of social awareness, where the age groups of 31–45 years were found to have better odds of higher social awareness than their younger and older counterparts (<30 years and >45 years age groups), respectively. This could be due to the increased age-related experience and countermeasures adopted owing to more exposure to various challenging situations at both occupational and personal front, respectively.
Limitation and recommendation
The main limitation of our study was the challenge of the ongoing Covid19 pandemic which called in for modification in data collection methodology from offline to online [except for one hospital, all the other three hospitals involved in the study had an online method of data collection], which made it difficult to assess the identity of each study participant. Also, as the course of the pandemic kept progressing, the workload and work pattern kept changing as well which could have affected their EI scores. This may have been different had the pandemic pressure not been an added factor in their existing share of responsibilities.
Rotation of nurses from high work intensive to less work intensive departments, support from senior nursing staff/supervisors, periodic breaks or permitted leave, professional counseling sessions, or recognition in terms of monitory or kind helps reduce stress.
The training and academic curriculum of nursing professionals could include periodic sessions on stress management and EI building workshops to lessen their mental health burden and help improve their quality of care and resilience.
| Conclusions|| |
EI helps adapt to diverse work demands, especially in terms of resilience and coping strategies.
Also, increased work experience helped develop higher EI, which acted as an additional factor for improvisations. Implications of the findings can help in modifying the nursing curriculum, to include more training and EI skill-building workshops to help accustom to the many accepted coping measures. There is also a need to consider similar factors during staff recruitment and the inclusion of periodic monitoring and platform to address such grievances.
However, further studies are needed to provide a deeper analysis of components of EI and work efficacy which could be expanded using qualitative research methodologies.
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient(s) has/have given his/her/their consent for his/her/their relevant clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Dr Ryan Fernandez and Dr Tony Raj, St John's Research Institute, Bangalore for their continuous guidance in coordinating the usage of RedCap software for our study purpose. Also thanking all the authorities of our study institutions and participants for their cooperation and support.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3]