Skip to main content

A social network intervention to improve connectivity and burnout among psychiatry residents in an academic institution: a quasi-experimental study

Abstract

Background

Burnout is common among residents, which could be associated with their professional network characteristics. This study aimed to assess the social networks of psychiatry residents and develop an intervention to improve their network characteristics, burnout, and perception of the educational environment.

Methods

We recruited a cohort of 17 PGY-2 residents and assessed their social networks, burnout, and perception of the educational environment. After the baseline survey, we held a focus group with PGY-2 residents to discuss the results, their network characteristics, and interventions that can improve their relationships. The PGY-2 residents indicated that offering extracurricular opportunities to facilitate friendly interactions among the residents and faculty members would be the most feasible and acceptable intervention. Therefore, four “interest groups” for extracurricular activities were established. Residents and faculty members were invited to participate in interest groups to improve the network characteristics. Some PGY-2 residents and faculty members agreed to moderate interest group sessions (active members).

Results

After the intervention, active residents improved significantly in the perceived personal accomplishment subscale of the burnout inventory and their perception of the educational environment. Active faculty members also had a significant increase in their relationships with PGY-2 residents in one domain of social networks.

Conclusions

Enhancing relationships between residents and faculty members through participatory intervention and extracurricular activities can improve faculty-resident connectivity and residents’ perception of personal accomplishment and educational environment quality.

Peer Review reports

Introduction

Teaching hospitals are multi-purpose communities with different, and often conflicting, aims: providing quality care, conducting health research, and training competent physicians. In this environment, the well-being, burnout, and academic performance of residents, given their dual responsibility both as a physician and a trainee, is a matter of concern [1]. These complexities, in conjunction with long shifting hours and low payment in some teaching hospitals, predispose residents to burnout. Existing literature suggests that burnout is quite common among residents [2, 3], and leads to clinical errors, poor rapport with the patients, and lower academic performance [4]. Given the importance of the issue, several studies have tried different intervention approaches to reduce residents’ burnout which demonstrated different effectiveness [5].

Social network analysis (SNA) research has investigated the effect of social contagion and influence on different aspects of life [6]. Personal characteristics such as weight [7], behaviors [8], or even intrapsychic experiences such as happiness [9], are correlated, and in some studies causally related, to one’s network characteristics in various domains of social connectedness. SNA is a collection of methods that investigate relationships, interactions, and social structures. SNA could visualize interactions between actors in a social network and help recognize groups, influential actors, and relationship patterns. Alongside mapping the network, SNA could quantify network parameters such as the number of interactions each person receives (indegree) or contributes to (outdegree), and the role and position of an actor in the network (centrality measures). SNA not only helps to understand the communities but also provides the researchers with various tools to design interventions to change them [6, 10,11,12].

In health system research, SNA has indicated that network characteristics of the staff are associated with burnout and job satisfaction [13, 14]. In educational sciences, SNA has given the researchers valuable insights into how dissimilarities in students’ social network properties influence their academic performance, well-being, and perception of accomplishment [15, 16]. Some consider social networks as an important part of the hidden curriculum [17,18,19]. However, despite the growing utilization of this method in other fields, SNA studies in the field of health education are rare [20].

In this study, we aimed to elicit the social network of a class of residents in an academic psychiatric hospital (Roozbeh) in Tehran, Iran. We also intended to develop and implement a participatory intervention informed by network analysis to improve residents’ burnout and their perception of the educational environment.

Methods

Context and participants

The physician community of the hospital consists of faculty members and psychiatry residents. Between 16 and 20 physicians start their psychiatry residency program at the hospital each year. The psychiatry residency is a four-year, competency-based program with a combination of supervised clinical experiences in different settings and a comprehensive didactic program. The pillars of the residency curriculum are quite similar to most North American programs. The resident-faculty relationship is usually limited to clinical settings, seminars, journal clubs, and other didactic sessions.

Baseline and follow up surveys

The baseline survey took place in December 2018. At the time, the hospital physician network consisted of 17 PGY-2 residents (participants), 34 PGY-3 and PGY-4 residents, and 47 faculty members. The follow-up survey took place in January 2020. The PGY-2 residents completed the study questionnaire at baseline and follow-up, which included the social networks questionnaire, the Maslach Burnout Inventory (MBI), and the Postgraduate Hospital Educational Environment Measure (PHEEM).

To assess the social networks and develop the intervention, we approached PGY-2 residents because they had already spent a year together building some relationships and had enough time ahead to get engaged in an intervention. PGY-2 residents were asked to nominate other residents and faculty members from a roster of hospital physicians with whom they had a social relationship in three different domains (clinical advice, educational advice, and personal support) in the previous month.

The survey also included the Maslach Burnout Inventory (MBI) [21] and the Postgraduate Hospital Educational Environment Measure (PHEEM) [22]. The MBI measures burnout in three dimensions: emotional exhaustion, depersonalization, and perceived sense of personal accomplishment. The PHEEM is a 40-item questionnaire designed specifically for assessing residents’ perceptions of the hospitals’ teaching environment. The reliability and validity of the Persian version of both instruments had been evaluated previously [23, 24].

Intervention design and implementation

The intervention was designed in a participatory manner. After the baseline survey and data extraction, we held a focus group with the PGY-2 residents. Fifteen out of 17 PGY-2 residents attended the focus group. The network maps and a brief report on burnout levels and PHEEM scores were presented to the PGY-2 residents. The PGY-2 residents were asked to brainstorm about the networks and interventions that can improve their relationship and perceived support.

According to the PGY-2 residents, their networks with each other were acceptably dense and balanced and needed no intervention. They also felt they had enough access to senior residents, and the amount of interaction was satisfactory. However, the PGY-2 residents felt a need for more interaction with faculty members, especially in a more informal manner. After more discussion about network characteristics, PGY-2 residents extensively reviewed potential solutions. PGY-2 residents indicated that offering extracurricular opportunities to facilitate friendly interactions among residents and faculty members would be the most feasible and acceptable intervention. Therefore, four “interest groups” focusing on extracurricular activities (psychiatry and literature, psychiatry and cinema, psychiatry and philosophy, and hiking) were established, and residents and faculty members were invited to participate. There was no limitation on the number of interest groups that residents and faculty members could attend.

To ensure appropriate management of the interest groups, one or two volunteer PGY-2 residents and one or two faculty members were assigned to moderate each interest group. This group will be called “active PGY-2 residents” and “active faculty members”, collectively called “active members”. The non-active members were also invited to and could attend all interest groups’ meetings; the only difference was involvement in interest groups’ moderation. Seven PGY-2 residents and six faculty members volunteered to serve as active members. Each interest group was expected to hold one meeting per month. The investigators did not intervene in the interest group’s management. During the seven-month intervention period, three interest groups held six meetings each, and one held five meetings.

Analysis

The network maps and indicators were created using UCINET software [25]. Two groups of networks were considered in the analysis: the relationship of PGY-2 residents within their cohort (one-mode networks), and the relationship of PGY-2 residents with faculty members (two-mode networks). The intervention aimed to improve connectivity between PGY-2 residents and faculty members; therefore, we excluded PGY-2 residents’ relationships with senior residents from the analysis.

We calculated the following indicators of the network structure: density (the number of existing ties to total possible ties), reciprocity (the number of bidirectional relationships to total existing relationships), and indegree centralization (ranges between zero and one, which measures the extent ties are focused on one or a few people). The significance of network structure differences between baseline and follow-up was calculated via bootstrap t test with 5000 samples using UCINET software.

We developed mixed-effect linear regression models with restricted maximum likelihood ratios to assess the changes in outcomes (the indegree of each member in networks, PHEEM, and MBI dimensions scores). For each model, the predictors included the “active vs. non-active members” and a variable indicating “baseline vs. follow-up” and their interaction. In models assessing PGY-2 residents’ changes, the PGY-2 resident’s ID number and in the models assessing faculty members’ changes, the faculty member’s ID number, were considered as the random effect. The models were developed by the “lme4” and “lmeTest” packages of R software version 4.03 [26, 27].

Results

Networks of PGY-2 residents

All 17 PGY-2 residents agreed to participate in the study (14 females and three males). The median age of PGY-2 residents was 32 years. One was excluded from the study because of failing the annual evaluation exam resulting in a different educational trajectory at follow-up.

Figure 1 shows the PGY-2 residents’ network maps based on all relationship types at baseline and follow-up. Relationship-specific network maps are provided in Additional file 1: supplementary figure S1-S3. The maps are one-mode networks that show only connections among the PGY-2 residents’ cohort. As shown in Fig. 1; Table 1, all three network types were relatively well connected and dense at baseline. The personal support network displayed the largest density (0.45), and reciprocity (0.28). Indegree centralizations in all three network types were small, which implies that the connections were evenly distributed and not monopolized by a few PGY-2 residents.

At follow-up, the densities slightly decreased, which was statistically significant in the personal support network. In all three networks, we observed a decrease in reciprocity and a mild elevation in indegree centralization, implying a tendency towards hierarchy.

Fig. 1
figure 1

The PGY-2 residents’ networks(one-mode) at baseline (A) and follow-up (B). Each node represents one PGY-2 resident, and each arrow is a nomination (personal support or clinical or educational advice). The direction of the arrow is from the nominator to the nominee. The size of each node is proportionate to its indegree. The red nodes are PGY-2 residents who volunteered to act as interest group moderators (active PGY-2 residents)

Table 1 The structural indicators of different PGY-2 residents’ (one-mode) networks at baseline and follow-up

Participation of active and non-active members

The difference in interest group attendance between active and non-active members was significant for both PGY-2 residents and faculty members (P values < 0.01). Non-active PGY-2 residents, on average, attended 1.3 sessions, while active PGY-2 residents attended eight sessions. Similarly, the average attendance for non-active faculty members was 0.7 sessions, and for active faculty members was 6.8 sessions.

Active and non-active members at baseline

Active PGY-2 residents did not differ from other PGY-2 residents in any network properties at baseline (Table 2). However, they had significantly higher scores in depersonalization and lower scores of perceived personal accomplishments in burnout sub-scales.

Active faculty members had significantly more relationships with PGY-2 residents than their non-active peers in clinical advice and educational advice networks at baseline (Table 2).

Table 2 The intercept and coefficients of linear mixed-effect regressions for different outcomes and their statistical significance

Active and non-active members over time

Active PGY-2 residents reported a significant increase in their perceived personal accomplishment in the MBI and PHEEM scores compared to their baseline (Table 2). While non-active PGY-2 residents reported significantly higher emotional exhaustion, lower perceived personal accomplishment, and lower PHEEM scores compared to their baseline. The differential improvement of active vs. non-active PGY-2 residents with respect to PHEEM scores and personal accomplishment was also statistically significant. Active PGY-2 residents had a decrease in indegree in the one-mode networks (connection within their cohort), which was significant in personal support and clinical advice networks, while the decrease in indegree of non-active PGY-2 residents was not statistically significant.

Active faculty members had significantly more relationships with PGY-2 residents in the personal support network compared to baseline. No significant change was observed in the network indicators of non-active faculty members. The differential increase of active faculty members’ relationships vs. non-active faculty members in the personal network over time was statistically significant.

Discussion

To the best of our knowledge, this study is the first to implement an intervention to improve residents’ burnout and their perception of the educational environment informed by SNA. We examined the social network of PGY-2 psychiatry residents in a teaching hospital, which showed an evenly distributed network between PGY-2 residents and limited interactions with faculty members. In the focus group discussion, the PGY-2 residents expressed their desire to improve their relationships with the faculty members. Our intervention successfully engaged a group of PGY-2 residents and faculty members as champions and group moderators of interest groups (active members). After seven months of the intervention, we found that the intervention significantly increased the relationships of active faculty members with PGY-2 residents and improved burnout and perception of the educational environment in active PGY-2 residents.

Impact on burnout and educational environment perception

The results show that the intervention was particularly beneficial for active PGY-2 residents, who had an improved perception of personal accomplishment at follow-up. This finding is in concordance with a published cross-sectional study indicating that out of burnout sub-scales, only perception of personal accomplishment is associated with social network position [28]. Interestingly, a systematic review that studied interventions addressing burnout among residents concluded that interventions by approaches other than SNA would improve emotional exhaustion and, to some degree, depersonalization sub-scales. The reported interventions emphasized workload reduction and self-care behaviors [5]. It can be concluded that interventions that aim to improve connectedness may address an aspect of burnout syndrome that otherwise remains untouched- the perception of personal accomplishment, which needs to be confirmed by further studies.

Active PGY-2 residents also reported an improved perception of the educational environment. This finding is in line with existing literature. We know that while faculty-student relationships affect students’ performance, they are usually scarce and limited to academic interactions [29]. Studies have shown that providing opportunities for interaction between students and faculty members facilitates the development of chemistry and mentor-mentee relationships [30,31,32,33]. The faculty-resident relationship is identified as an important determinant of residents’ well-being [34]; and similarly, having seniors in the social network will help students’ academic performance [35].

Unlike their peers, non-active PGY-2 residents reported significantly higher emotional exhaustion and a declining perception of personal accomplishment and of the educational environment quality. During the intervention period, several stressful events happened in the country, such as a serious economic crisis that posed stress to the public and might have contributed to this higher burnout in non-active PGY-2 residents. But it is also possible that the intervention design per se could be partly responsible for the unfavorable outcome observed in non-active PGY-2 residents. A similar pattern was seen in another study aimed to improve self-care behavior in residents by an intervention involving some residents and informing other residents about their progress via email, hypothesizing that the emails would improve other residents’ self-care by social contagion. The authors found that other residents who were not directly involved in the intervention neither welcomed nor benefited from emails [36]. The researchers attributed this effect to the “fear of missing out” [37] and the “feeling of inferiority when comparing oneself to others” [38].

Impact on the networks

The intervention successfully increased the relationships of active faculty members with PGY-2 residents. At follow-up, active faculty members had a significant increase in their relationships with PGY-2 residents at the personal support network. Although, this improvement did not extend to the non-active faculty members’ relationships. It is not unusual in social network studies that interventions impact actors differently. The concept of preferential attachment suggests that the actors with more social ties have more chance of developing new ties due to intervention, a phenomenon that results in a “rich get richer” pattern after network interventions [39]. This implies that in any network intervention, special attention should be paid to involving actors in the network periphery.

At the time of preparing this manuscript (one year after follow-up surveys), three out of four interest groups are still running while investigators had no contribution to it. The interest groups survived the COVID-19 pandemic by using online interfaces. This indicates that the effects of the intervention may have gone beyond what we have reported here.

Limitations

We faced some limitations in our study, namely, the quasi-experimental design without a control group which was implemented in a real-life environment. There were numerous confounding variables that we were unable to control. Therefore, the conclusions we made in this manuscript are merely hypotheses in need of further confirmation in controlled interventional studies. Our small sample size limits the statistical power of the study. Furthermore, we did not assess the quality (frequency and intensity) of relationships, which may have changed due to intervention.

Conclusions

Enhancing relationships between residents and faculty members through participatory intervention and extracurricular activities can improve faculty-resident connectivity and residents’ perception of personal accomplishment and of the educational environment quality.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to privacy and ethical restrictions but are available from the corresponding author upon reasonable request.

Abbreviations

SNA:

social network analysis

PGY:

postgraduate year

MBI:

Maslach Burnout Inventory

PHEEM:

Postgraduate Hospital Educational Environment Measure

References

  1. Raj KS. Well-Being in Residency: A Systematic Review. J Grad Med Educ. 2016;8(5):674–84.

    Article  Google Scholar 

  2. Low ZX, Yeo KA, Sharma VK, Leung GK, McIntyre RS, Guerrero A, et al. Prevalence of Burnout in Medical and Surgical Residents: A Meta-Analysis. Int J Environ Res Public Health. 2019;16(9):1479. Available from: https://www.mdpi.com/1660-4601/16/9/1479.

  3. Sadeghi A, Asgari AA, Bagheri A, Zamzam A, Soroush AR, Khorgami Z. Medical Resident Workload at a Multidisciplinary Hospital in Iran. Res Dev Med Educ. 2014;3(2):73–7.

    Google Scholar 

  4. Rodrigues H, Cobucci R, Oliveira A, Cabral JV, Medeiros L, Gurgel K, et al. Burnout syndrome among medical residents: A systematic review and meta-analysis. PLoS One. 2018;13(11):1–17.

    Article  Google Scholar 

  5. Busireddy KR, Miller JA, Ellison K, Ren V, Qayyum R, Panda M. Efficacy of Interventions to Reduce Resident Physician Burnout: A Systematic Review. J Grad Med Educ. 2017;9(3):294–301.

    Article  Google Scholar 

  6. Christakis NA, Fowler JH. Social contagion theory: Examining dynamic social networks and humanbehavior. Stat Med. 2013;32(4):556–77.

    Article  Google Scholar 

  7. Christakis NA, Fowler JH. The Spread of Obesity in a Large Social Network over 32 Years. N Engl J Med. 2007;357(4):370–9.

    Article  Google Scholar 

  8. Edge R, Heath J, Rowlingson B, Keegan TJ, Isba R. Seasonal influenza vaccination amongst medical students: A social network analysis based on a cross-sectional study. PLoS One. 2015;10(10):1–13.

    Article  Google Scholar 

  9. Fowler JH, Christakis NA. Dynamic spread of happiness in a large social network: Longitudinal analysis over 20 years in the Framingham Heart Study. BMJ. 2009;338(7685):23–6.

    Google Scholar 

  10. Valente TW. Network interventions. Science (80-). 2012;336(6090):49–53.

    Article  Google Scholar 

  11. Saqr M, Alamro A. The role of social network analysis as a learning analytics tool in online problem based learning. BMC Med Educ. 2019;19(1):160. Available from: https://bmcmededuc.biomedcentral.com/articles/https://doi.org/10.1186/s12909-019-1599-6

  12. Serrat O. Knowledge Solutions. Knowledge Solutions: Tools, Methods, and Approaches to Drive Organizational Performance. Singapore: Springer Singapore; 2017. 1–1140 p. Available from: http://link.springer.com/https://doi.org/10.1007/978-981-10-0983-9

  13. Emmerik I van, Euwema M. Healthy networking: Effects of social support and network characteristics on burnout among medical faculty. ResearchgateNet. 2014;(May). Available from: https://www.researchgate.net/profile/IJH_Emmerik/publication/46648031_Healthy_networking._Effects_of_social_support_and_network_characteristics_on_burnout_among_medical_faculty/links/0912f508e8d109ce88000000.pdf

  14. Tasselli S. Social networks of professionals in health care organizations: A review. Med Care Res Rev. 2014;71(6):619–60.

    Article  Google Scholar 

  15. Grunspan DZ, Wiggins BL, Goodreau SM. Understanding classrooms through social network analysis: A primer for social network analysis in education research. CBE Life Sci Educ. 2014;13(2):167–78.

    Article  Google Scholar 

  16. Penuel WR, Korbak C, Hoadley C. Investigating the potential of using social network analysis in educational evaluation. Am J Eval. 2006;27(4):437–51.

    Article  Google Scholar 

  17. Hommes J, Rienties B, de Grave W, Bos G, Schuwirth L, Scherpbier A. Visualising the invisible: a network approach to reveal the informal social side of student learning. Adv Heal Sci Educ. 2012;17(5):743–57. Available from: http://link.springer.com/https://doi.org/10.1007/s10459-012-9349-0

  18. Hafferty FW, Castellani B. The hidden curriculum. A Theory Med Educ Handb Sociol Med Educ C Brosnan, BS Turn (red), Rutledge London, New York. 2009;15–35.

  19. Woolf K, Potts HWW, Patel S, McManus IC. The hidden medical school: A longitudinal study of how social networks form, and how they relate to academic performance. Med Teach. 2012;34(7):577–86.

    Article  Google Scholar 

  20. Isba R, Woolf K, Hanneman R. Social network analysis in medical education. Med Educ. 2017;51(1):81–8.

    Article  Google Scholar 

  21. Maslach C, Jackson SE, Leiter MP. Maslach Burnout Inventory: Manual (fourth edition). Menlo Park: Mind Garden Inc; 2018. Available from: https://www.mindgarden.com/117-maslach-burnout-inventory-mbi.

  22. Roff S, McAleer S, Skinner A. Development and validation of an instrument to measure the postgraduate clinical learning and teaching educational environment for hospital-based junior doctors in the UK. Med Teach. 2005;27(4):326–31.

    Article  Google Scholar 

  23. Jalili M, Hejri SM, Ghalandari M, Moradi-Lakeh M, Mirzazadeh A, Roff S. Validating modified PHEEM questionnaire for measuring educational environment in academic emergency departments. Arch Iran Med. 2014;17(5):372–7.

    Google Scholar 

  24. Shamloo ZS, Hashemian SS, Khoshsima H, Shahverdi A, Khodadost M, Gharavi MM. Validity and reliability of the persian version of the maslach burnout inventory (General Survey Version) in Iranian population. Iran J Psychiatry Behav Sci. 2017;11(2).

  25. Borgatti SP, Everett MG, Freeman LC. UCINET 6 for Windows: Software for social network analysis (Version 6.102). Harvard, MA, Analytic Technologies. 2002.

  26. Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67(1).

  27. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: Tests in Linear Mixed Effects Models. J Stat Softw. 2017;82(13).

  28. Shapiro J, Zhang B, Warm EJ. Residency as a Social Network: Burnout, Loneliness, and Social Network Centrality. J Grad Med Educ. 2015;7(4):617–23.

    Article  Google Scholar 

  29. Cotten SR, Wilson B. Student–faculty Interactions: Dynamics and Determinants. High Educ. 2006;51(4):487–519. Available from: http://link.springer.com/https://doi.org/10.1007/s10734-004-1705-4

  30. Benjamin M, Griffin KA. “Pleasantly unexpected”: The Nature and Impact of Resident Advisors’ Functional Relationships with Faculty. J Stud Aff Res Pract. 2013;50(1):56–71. Available from: https://www.degruyter.com/document/doi/https://doi.org/10.1515/jsarp-2013-0004/html

  31. Sanfey H, Hollands C, Gantt NL. Strategies for building an effective mentoring relationship. Am J Surg. 2013;206(5):714–8. Available from: https://doi.org/10.1016/j.amjsurg.2013.08.001

  32. Stack SJ, Watson MJ, Newman DH. Enriching the resident-faculty relationship. Ann Emerg Med. 2001;38(3):336–8. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0196064401439151

  33. McKenna AM, Straus SE. Charting a professional course: a review of mentorship in medicine. J Am Coll Radiol. 2011;8(2):109–12. Available from: https://doi.org/10.1016/j.jacr.2010.07.005

  34. Zhang LM, Cheung EO, Eng JS, Ma M, Etkin CD, Agarwal G, et al. Development of a conceptual model for understanding the learning environment and surgical resident well-being. Am J Surg. 2021;221(2):323–30. Available from: https://doi.org/10.1016/j.amjsurg.2020.10.026

  35. Vaughan S, Sanders T, Crossley N, O’Neill P, Wass V. Bridging the gap: The roles of social capital and ethnicity in medical student achievement. Med Educ. 2015;49(1):114–23.

    Article  Google Scholar 

  36. Chisolm MS, Tackett S, Insetta E, Ruble A, Wright S. An Attempt to Harness the Power of Social Networks for a Graduate Medical Education Curricular Intervention. Acad Psychiatry. 2019;43(5):549–50.

    Article  Google Scholar 

  37. Przybylski AK, Murayama K, Dehaan CR, Gladwell V. Motivational, emotional, and behavioral correlates of fear of missing out. Comput Human Behav. 2013;29(4):1841–8. Available from: https://doi.org/10.1016/j.chb.2013.02.014

  38. Park SY, Baek YM. Two faces of social comparison on Facebook: The interplay between social comparison orientation, emotions, and psychological well-being. Comput Human Behav. 2018;79:83–93. Available from: https://doi.org/10.1016/j.chb.2017.10.028

  39. Perc M. The Matthew effect in empirical data. J R Soc Interface. 2014;11(98):20140378. Available from: https://doi.org/royalsocietypublishing.org/doi/10.1098/rsif.2014.0378

Download references

Acknowledgements

The authors would like to thank all residents and faculty members of Roozbeh hospital for their kind participation in this study.

Funding

Tehran University of Medical Sciences funded this study under contract number 97-03-30-40363. This study was Dr. Ardavan Mohammad Aghaei’s postgraduate thesis in psychiatry.

Author information

Affiliations

Authors

Contributions

MT, VSH, AMA, and RYN developed the original idea and contributed to the study design and protocol. AMA and FAM were responsible for data acquisition and management. MT conducted the focus group. RYN and AMA analyzed the data. AMA, MT, and RYN prepared the manuscript draft. All the authors critically reviewed the manuscript and approved the final version.

Corresponding author

Correspondence to Maryam Tabatabaee.

Ethics declarations

Ethics approval and consent to participate

All methods were carried out in accordance with Tehran University of Medical Sciences guidelines and regulations. The study questionnaires and protocol are approved by the ethical committee of the Tehran University of Medical Sciences (IR.TUMS.MEDICINE.REC.1398.433). Participation in this study was voluntary. Written informed consent was obtained from participants. We took into account different considerations to protect participants’ anonymity.

Consent for publication

Not applicable.

Competing interests

None.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Aghaei, A.M., Sharifi, V., Tabatabaee, M. et al. A social network intervention to improve connectivity and burnout among psychiatry residents in an academic institution: a quasi-experimental study. BMC Med Educ 22, 367 (2022). https://doi.org/10.1186/s12909-022-03440-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12909-022-03440-5

Keywords

  • Burnout
  • Medical education
  • Residency program
  • Hospital education environment
  • Social network analysis