Peer relationships buffer the negative association of online education with education satisfaction and subsequently with study engagement among undergraduate medical students
BMC Medical Education volume 22, Article number: 276 (2022)
Due to the COVID-19 pandemic, undergraduate medical students had to follow high amounts of online education. This does not match their preferences and might negatively affect their education satisfaction and study engagement. As low levels of education satisfaction and study engagement are risk factors for burnout and dropout, resources that mitigate these possible negative consequences of forced online education need to be identified. Therefore, the current study investigated 1) the associations of the amount of online education with education satisfaction and study engagement, and 2) whether quantitative (i.e., network size) and qualitative (i.e., perceived support) aspects of peer relationships can buffer the expected negative associations.
In a cross-sectional study, 372 undergraduate medical students from all eight Dutch medical schools (79.8% female; mean age: 20.4 years) completed an online survey assessing the relevant variables. Data were analysed using correlation and moderated mediation analyses.
The amount of online education was significantly negatively related to education satisfaction and study engagement. Additionally, higher amounts of online education were indirectly associated with lower levels of study engagement through lower levels of education satisfaction. More importantly, both quantitative and qualitative aspects of peer relationships significantly buffered this negative indirect association. Specifically, among medical students with a large peer network or high levels of perceived peer support, the amount of online education was no longer significantly negatively related to education satisfaction and subsequently to study engagement.
The current study underlines the importance of peer relationships in the educational context, since our findings indicate that both the peer network size and the perceived peer support protect medical students’ education satisfaction and study engagement when confronted with study demands, such as forced online education during the COVID-19 pandemic. These findings may be translated into educational efforts that stimulate collaborative learning and the formation of formal peer networks.
Due to the COVID-19 pandemic, undergraduate medical students were forced to complete high amounts of their education online as during the lockdowns, the educational offer mainly consisted of recorded lectures and knowledge clips, online discussion boards, and lectures and tutorials via collaboration and video conferencing platforms such as Zoom, Teams, and Kaltura . However, empirical findings indicate that medical students prefer conventional face-to-face and blended education over online education [2,3,4,5]. Thus, online education during the COVID-19 pandemic did not match the preferences of medical students and can therefore be considered as an external stressor or study demand. Such a distressing mismatch can potentially lead to lower levels of education satisfaction and study engagement [6,7,8,9,10,11], which are important risk factors for burnout and dropout [12, 13]. These negative outcomes should be prevented as they are associated with severe consequences for students (e.g., economic loss, reduced self-confidence) [14,15,16] and the healthcare system (e.g., future lack of physicians, lower quality of care) [17,18,19]. Therefore, factors that can mitigate or even eliminate the potential negative impact of online education on education satisfaction and study engagement need to be identified and used to make recommendations about the design of online education.
Impact of online education on education satisfaction and study engagement
Several theoretical frameworks, e.g., the theory of educational productivity  and the distance education student satisfaction model , underline the importance of the study context and whether it matches the preferences of students as determinants of study-related outcomes [20,21,22,23,24]. Similarly, previous empirical findings have demonstrated that a mismatch between the study context and personal preferences lowers education satisfaction [2, 6,7,8]. Additionally, empirical studies based on the job demands-resources model applied to the study context have shown that study demands are negatively associated with study satisfaction and study engagement [12, 25,26,27].
Little is known about the direction of the relationship between education satisfaction and study engagement. Previous occupational research has identified job satisfaction as an antecedent of work engagement [28,29,30]. Moreover, it has been theoretically argued and empirically supported that job satisfaction is a mediator between job characteristics and work-related outcomes [28, 31]. These findings suggest that study demands might be related to lower study engagement through lower education satisfaction. This mediating process has not yet been investigated in the context of study stress resulting from a mismatch between study preferences and the actual study context.
Peer relationships as a buffer of the negative effects of forced online education on education satisfaction and study engagement
Various theoretical approaches, e.g., the stress-buffering hypothesis  and the job demands-resources model , consider quantitative and qualitative aspects of social relationships as resources that can protect individuals from diverse negative effects of external stressors. Empirical studies among (medical) students and healthcare professionals have confirmed that social relationships can mitigate or even eliminate the effects of demands or stressors on outcomes such as burnout, well-being, motivation, exhaustion, and academic or job performance [34,35,36,37,38,39,40,41]. By applying these findings to the context of online education during the COVID-19 pandemic, social relationships with fellow students might protect medical students from the negative consequences of large amounts of forced online education, such as reduced education satisfaction and study engagement.
The present study
The first aim of this study was to examine the relationships among the amount of online education, education satisfaction, and study engagement in undergraduate medical students. It was expected that a higher amount of online education is related to lower levels of education satisfaction and study engagement. It was also hypothesized that education satisfaction and study engagement are positively associated and that education satisfaction serves as a mediator between the amount of online education and study engagement. The second aim was to investigate whether quantitative (i.e., network size) and qualitative (i.e., perceived support) aspects of peer relationships moderate the expected indirect association between the amount of online education and study engagement through education satisfaction. It was expected that both peer network size and perceived peer support buffer this negative indirect association. More specifically, it was hypothesized that the negative indirect effect of the amount of online education on education satisfaction and subsequently on study engagement becomes weaker or disappears with an increasing peer network size and higher levels of perceived peer support. Figure 1 graphically represents the hypothesized moderated mediation relationships.
This cross-sectional study was conducted during the lockdown in April 2021 via an online survey programmed in Qualtrics. Undergraduate medical students from all eight registered Dutch medical schools were eligible to participate. The participants were recruited via posts on social media (e.g., Facebook groups of medical students, Instagram, LinkedIn) and emails sent to members of various medical student associations. After opening the link to the questionnaire, participants had to complete an informed consent that highlighted their voluntary participation and anonymity. As compensation, participants who fully completed the questionnaire could take part in a raffle for gift vouchers. The study protocol was approved by the Medical Ethics Review Committee of the Erasmus Medical Center (#2020–0815).
The survey was administered in Dutch. The means and standard deviations for all measures are shown in Table 1.
The amount of online education was measured by a single item, i.e., “Since the beginning of the current academic year, what percentage of all your study activities has taken place online?”. Responses were given as a percentage ranging from 0 to 100%.
Education satisfaction was measured by a single item, i.e., “All things considered, how satisfied are you with your education since the beginning of the current academic year as a whole?”. Responses were given on an 11-point scale ranging from 0 (extremely dissatisfied) to 10 (extremely satisfied). Previous research supports the use of a single item to measure satisfaction [42, 43].
Study engagement since the beginning of the academic year was measured by the ultra-short Utrecht Work Engagement Scale (UWES-3), consisting of three items [44, 45]. The three items were adapted for application to the students’ academic lives. Responses were given on a 7-point scale from 0 (never) to 6 (always). Items were averaged, with higher scores indicating greater study engagement. In the current study, Cronbach’s alpha was .61.
Peer network size
The peer network size was measured by a single item, i.e., “With how many fellow students did you have frequent study-related and/or personal contact in the last month? This may have been face-to-face, but also digital.”. Responses were registered in an open-ended numerical format. The use of such a single-item measure for network size has been validated in previous research .
Perceived peer support
Perceived peer support since the beginning of the academic year was measured by using a three-item measure, focusing on emotional, informational, and practical support . The responses were given on a 7-point scale from 0 (never) to 6 (always). Items were averaged, with higher scores indicating greater perceived peer support. Previous research supports the reliability and validity of this scale to measure perceived peer support . In the current study, Cronbach’s alpha was .85.
Statistical analyses were performed with IBM SPSS Statistics version 27  and PROCESS macro for SPSS version 3.5.3 . First, bivariate associations between the study variables were analysed using Pearson correlation analysis. Subsequently, two moderated mediation analyses (one for each moderator) with the amount of online education as the independent variable, education satisfaction as the mediator, study engagement as the dependent variable, and peer network size or perceived peer support as the moderator of the relation between the amount of online education and education satisfaction and study engagement were calculated using Hayes’ model 8 (Fig. 1).
The moderated mediation analysis contained the following subanalyses. First, a multiple regression analysis was conducted with education satisfaction as the dependent variable to estimate the effects of the amount of online education, the moderator (peer network size or perceived peer support) as well as the interaction effect between online education and the moderator. In the case of a significant interaction effect, the conditional effects of the amount of online education on education satisfaction were calculated for a low, medium, and high level (i.e., −1SD, mean, and + 1SD) of the moderator. Second, a multiple regression analysis was conducted with study engagement as the dependent variable to estimate the effects of the amount of online education, education satisfaction, the moderator (peer network size or perceived peer support) as well as the interaction effect between online education and the moderator. In the case of a significant interaction effect, the conditional effects of the amount of online education on study engagement were calculated for a low, medium, and high level (i.e., −1SD, mean, and + 1SD) of the moderator. Third, the index of moderated mediation was determined using bootstrapping with 5000 bootstrap samples, which indicated whether the indirect effect of the amount of online education on study engagement through education satisfaction varies depending on the level of the moderator. In the case of a significant index of moderated mediation, the conditional indirect effects of the amount of online education on study engagement through education satisfaction were estimated for a low, medium, and high level (i.e., −1SD, mean, and + 1SD) of the moderator, also using bootstrapping with 5000 bootstrap samples. Moreover, pairwise comparisons of the indirect effects were calculated to examine which of the indirect effects differ significantly from each other. Unstandardized coefficients are reported.
A total of 637 medical students started the survey. After screening out respondents who did not meet the inclusion criteria (n = 68) and excluding respondents who did not finish the questionnaire (n = 153) or failed one of the various reliability checks, such as an inadequate processing time or implausible answers (n = 44), 372 participants were included in the analysis.
The sample consisted of 297 women (79.8%), 73 men (19.6%), and 2 individuals who identified as nonbinary (0.5%), with a mean age of 20.4 years (SD = 1.81, range: 17–31 years). Students from all eight registered Dutch medical schools participated and were in their first (29.6%), second (37.6%), or third (32.8%) undergraduate year. Most of the participants lived in student housing (41.7%) or with their parents/family (34.9%), followed by living alone (10.2%) and with friends (7.0%). The rest lived with their partner (4.0%), rented from a landlady (0.3%), or in another form (1.9%).
Bivariate associations between study variables
The results of the correlation analyses are presented in Table 1. As expected, the amount of online education was significantly negatively associated with education satisfaction and study engagement. In addition, education satisfaction and study engagement were significantly positively related.
Moderated mediation analyses
The multiple regression analyses with education satisfaction as the dependent variable (Table 2, columns 2 and 3) revealed significant interaction effects between the amount of online education and both moderators, indicating that the effect of the amount of online education on education satisfaction depends on the peer network size and the level of perceived peer support. More specifically, the conditional effects demonstrated that the amount of online education was significantly negatively related to education satisfaction at a low and medium level of peer network size or perceived peer support, but this relationship was no longer significant at a high level of peer network size or perceived peer support (Table 3, columns 2 and 3).
The multiple regression analyses with study engagement as the dependent variable (Table 2, columns 4 and 5) revealed nonsignificant interaction effects between the amount of online education and both moderators, indicating that the direct effect of the amount of online education on study engagement does not depend on peer relationships; this effect was not significant. However, a significant positive effect of education satisfaction on study engagement was found.
The index of moderated mediation was significant in both moderated mediation analyses with peer network size as the moderator, b = 0.0007, 95% CI (0.0001, 0.0017), and with perceived peer support as the moderator, b = 0.0038, 95% CI (0.0011, 0.0075). These significant indices indicate that the negative indirect effect of the amount of online education on study engagement through education satisfaction depends on the peer network size and the level of perceived peer support. Additionally, the pairwise comparisons between the conditional indirect effects were all significant. Overall, the negative indirect effect of the amount of online education on study engagement via education satisfaction became weaker with a larger peer network size or higher levels of perceived peer support (Table 3, columns 4 and 5). More specifically, the amount of online education had a significant negative effect on study engagement through lower education satisfaction at a low and medium level of peer network size or perceived peer support; however, this effect was no longer significant at a high level of peer network size or perceived peer support.
The results did not change significantly after controlling for age and gender (data not shown).
The present study found that online education was negatively associated with both education satisfaction and study engagement. These findings are in line with previous results indicating that forced online education has a negative effect on study-related outcomes, such as education satisfaction and study engagement [2, 6,7,8,9,10]. Moreover, the present findings extend previous research [28,29,30,31] by revealing mediating processes, i.e., the negative effect of online education on study engagement is fully mediated by education satisfaction. In agreement with several theoretical frameworks [20, 21] and empirical results [2, 6,7,8, 20,21,22,23,24], the present findings underline the importance of the study context and whether it matches the preferences of medical students, since a mismatch negatively affects their education satisfaction and subsequently their study engagement.
Most importantly, the present study identified peer relationships as a resource that can mitigate or even eliminate the negative effects of forced online education. As expected, both quantitative and qualitative aspects of peer relationships moderated the negative indirect effect of the amount of online education on study engagement through education satisfaction. More specifically, in medical students with large peer networks or with high levels of perceived peer support, online education was not associated with lower education satisfaction and subsequently lower study engagement. These results are in line with various theoretical frameworks [32, 33] and previous empirical studies [34,35,36,37,38,39,40,41], and emphasize the importance of peer relationships in the educational context.
The results of the present study demonstrate that medical students enrolled in forced online education programs benefit from having a large peer network and from perceiving high levels of peer support. Consequently, medical schools should focus on facilitating opportunities for medical students to have more frequent and more meaningful interactions with their peers when they are forced to complete high amounts of their education online . Implementing this physically without violating the governmental rules for social distancing during a lockdown is a challenge. Nevertheless, medical schools should stimulate social interactions between students via digital media in at least two ways. First, they should implement collaborative learning , e.g., use interactive education tools, create breakout rooms during online education, or set up more group assignments. Second, medical schools should also focus on setting up formal peer networks, for instance by implementing near-peer mentor groups , peer-led support programs [53, 54], peer support workshops , or virtual peer support group conferences . Both approaches can stimulate social interactions between students, thus leading to higher satisfaction among students in online learning environments .
The present results must be considered in light of certain study limitations. First, due to the cross-sectional design, the direction of causality between education satisfaction and study engagement could not be determined . Although the tested moderated mediation model is based on theoretical assumptions and empirical findings, it could not be excluded that the direction of the relationships between education satisfaction and study engagement might be reversed or reciprocal [58,59,60]. Second, education design characteristics (such as interactivity, collaboration, and synchronicity) were disregarded, despite their close relationship with education satisfaction and social relations [21, 24, 61, 62]. For example, education design characteristics can be moderating variables on the relationship between the amount of online education and education satisfaction . Third, the present convenience sample limited the generalizability of the findings. Due to the self-selected sampling, the extent to which this sample is representative of the population of Dutch undergraduate medical students remains unclear. However, students from all eight registered Dutch medical schools did participate. Also, the sample mainly consists of female participants, but this is largely in line with the composition of medical students in the Netherlands . Therefore, longitudinal and experimental studies with representative samples that also include education design characteristics are warranted in the future.
This work underlines the importance of the study context and whether it matches the preferences of medical students, since a mismatch has a negative impact on study-related outcomes. Specifically, the present findings indicate that an increase in the amount of online education can result in decreased study engagement through reduced education satisfaction, which are known risk factors for burnout and dropout. Most importantly, the current study shows the relevance of peer relationships in the educational context. Medical students with large peer networks or high levels of perceived peer support do not lose education satisfaction and study engagement when they are forced to complete a large percentage of their education online.
Sahu P. Closure of universities due to coronavirus disease 2019 (COVID-19): Impact on Education and mental health of students and academic staff. Cureus. 2020;12(4):e7541.
Dost S, Hossain A, Shehab M, Abdelwahed A, Al-Nusair L. Perceptions of medical students towards online teaching during the COVID-19 pandemic: a national cross-sectional survey of 2721 UK medical students. BMJ Open. 2020;10(11):e042378.
Hameed T, Husain M, Jain SK, Singh CB, Khan S. Online medical teaching in COVID-19 era: experience and perception of undergraduate students. Maedica (Bucur). 2020;15(4):440–4.
Alsoufi A, Alsuyihili A, Msherghi A, Elhadi A, Atiyah H, Ashini A, et al. Impact of the COVID-19 pandemic on medical education: medical students’ knowledge, attitudes, and practices regarding electronic learning. PLoS One. 2020;15(11):e0242905.
Abbasi S, Ayoob T, Malik A, Memon SI. Perceptions of students regarding E-learning during Covid-19 at a private medical college. Pak J Med Sci. 2020;36(COVID19-S4):S57–61.
Chandra Y. Online education during COVID-19: perception of academic stress and emotional intelligence coping strategies among college students. Asian Educ Dev Stud. 2021;10(2):229–38.
Fatonia NA, Nurkhayatic E, Nurdiawatid E, Fidziahe GP, Adhag S, Irawanh AP, et al. University students online learning system during Covid-19 pandemic: advantages, constraints and solutions. Syst Rev Pharm. 2020;11(7):570–6.
Nambiar D. The impact of online learning during COVID-19: students’ and teachers’ perspective. Int J Indian Psychol. 2020;8(2):783–93.
Robinson CC, Hullinger H. New benchmarks in higher education: student engagement in online learning. J Educ Bus. 2008;84(2):101–9.
van Wingerden J, Derks D, Bakker AB. Facilitating interns’ performance. Career Dev Int. 2018;23(4):382–96.
Dumford AD, Miller AL. Online learning in higher education: exploring advantages and disadvantages for engagement. J Comput High Educ. 2018;30(3):452–65.
Agarwal G, Mosquera M, Ring M, Victorson D. Work engagement in medical students: an exploratory analysis of the relationship between engagement, burnout, perceived stress, lifestyle factors, and medical student attitudes. Med Teach. 2020;42(3):299–305.
Liu H, Yansane AI, Zhang Y, Fu H, Hong N, Kalenderian E. Burnout and study engagement among medical students at sun Yat-sen University, China: a cross-sectional study. Medicine. 2018;97(15):e0326.
Faas C, Benson MJ, Kaestle CE, Savla J. Socioeconomic success and mental health profiles of young adults who drop out of college. J Youth Stud. 2018;21(5):669–86.
Hällsten M. Is education a risky investment? The scarring effect of university dropout in Sweden. Eur Sociol Rev. 2017;33(2):169–81.
Ortiz EA, Dehon C. Roads to success in the Belgian French Community's higher education system: predictors of dropout and degree completion at the Université Libre de Bruxelles. Res High Educ. 2013;54(6):693–723.
Bodenheimer T, Chen E, Bennett HD. Confronting the growing burden of chronic disease: can the U.S. health care workforce do the job? Health Aff. 2009;28(1):64–74.
Dall TM, Gallo PD, Chakrabarti R, West T, Semilla AP, Storm MV. An aging population and growing disease burden will require ALarge and specialized health care workforce by 2025. Health Aff. 2013;32(11):2013–20.
Dyrbye LN, Thomas MR, Power DV, Durning S, Moutier C, Massie FSJ, et al. Burnout and serious thoughts of dropping out of medical school: a multi-institutional study. Acad Med. 2010;85(1):94–102.
Walberg HJ. A psychological theory of educational productivity. In: Psychology and education: the state of the union. Edited by Gordon FFNJ. Berkeley: McCutchan; 1980. p. 81–108.
Sahin I, Shelley M. Considering Students' perceptions: the distance education student satisfaction model. J Educ Technol Soc. 2008;11(3):216–23.
Elliott KM, Shin D. Student satisfaction: an alternative approach to assessing this important concept. J High Educ Policy Manag. 2002;24(2):197–209.
Fraser BJ. Research on classroom and school climate. In: Handbook of research on science teaching and learning. edn. Edited by Gabel D; 1994. p. 493–541.
Nortvig A-M, Petersen AK, Balle SH. A literature review of the factors influencing E-learning and blended learning in relation to learning outcome, student satisfaction and engagement. Electron J E-learn. 2018;16(1):46–55.
Cilliers JR, Mostert K, Nel JA. Study demands, study resources and the role of personality characteristics in predicting the engagement of first-year university students. South African J Higher Educ. 2018;32(1):49–70.
Lesener T, Pleiss LS, Gusy B, Wolter C. The study demands-resources framework: an empirical introduction. Int J Environ Res Public Health. 2020;17(14):5183.
Kulikowski K, Antipow E, Potoczek A, Król S. How to survive in academia: demands, resources and study satisfaction among polish PhD students. Kuram ve Uygulamada Egitim Bilimleri. 2019;19(4):65–79.
Rayton BA, Yalabik ZY. Work engagement, psychological contract breach and job satisfaction. Int J Hum Resour Manag. 2014;25(17):2382–400.
Yalabik ZY, Popaitoon P, Chowne JA, Rayton BA. Work engagement as a mediator between employee attitudes and outcomes. Int J Hum Resour Manag. 2013;24(14):2799–823.
Simpson MR. Predictors of work engagement among medical-surgical registered nurses. West J Nurs Res. 2009;31(1):44–65.
Crede M, Chernyshenko OS, Stark S, Dalal RS, Bashshur M. Job satisfaction as mediator: an assessment of job satisfaction's position within the nomological network. J Occup Organ Psychol. 2007;80(3):515–38.
Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull. 1985;98(2):310–57.
Demerouti E, Bakker AB. The job demands-resources model: challenges for future research. SA J Ind Psychol. 2011;37(2):01–9.
Yamada Y, Klugar M, Ivanova K, Oborna I. Psychological distress and academic self-perception among international medical students: the role of peer social support. BMC Med Educ. 2014;14(1):256.
Pow J, King DB, Stephenson E, DeLongis A. Does social support buffer the effects of occupational stress on sleep quality among paramedics? A daily diary study. J Occup Health Psychol. 2017;22(1):71–85.
Seo J-H, Kim HJ, Kim B-J, Lee S-J, Bae H-O. Educational and relational stressors associated with burnout in Korean medical students. Psychiatry Investig. 2015;12(4):451–8.
de Jonge J, Le Blanc PM, Peeters MCW, Noordam H. Emotional job demands and the role of matching job resources: a cross-sectional survey study among health care workers. Int J Nurs Stud. 2008;45(10):1460–9.
Hu Q, Schaufeli WB, Taris TW. The Job Demands–Resources model: An analysis of additive and joint effects of demands and resources. J Vocat Behav. 2011;79(1):181–90.
Vander Elst T, Cavents C, Daneels K, Johannik K, Baillien E, Van den Broeck A, et al. Job demands–resources predicting burnout and work engagement among Belgian home health care nurses: a cross-sectional study. Nurs Outlook. 2016;64(6):542–56.
Knoster KC, Goodboy AK. A conditional process model of academic demands and student learning. Commun Educ. 2020;69(3):335–55.
Kaspersen M, Matthiesen SB, Götestam KG. Social network as a moderator in the relation between trauma exposure and trauma reaction: a survey among UN soldiers and relief workers. Scand J Psychol. 2003;44(5):415–23.
Jovanović V, Lazić M. Is longer always better? A comparison of the validity of single-item versus multiple-item measures of life satisfaction. Appl Res Qual Life. 2020;15(3):675–92.
Sears CR, Boyce MA, Boon SD, Goghari VM, Irwin K, Boyes M. Predictors of student satisfaction in a large psychology undergraduate program. Can Psychol. 2017;58(2):148–60.
Schaufeli WB, Shimazu A, Hakanen J, Salanova M, Witte HD. An ultra-short measure for work engagement. Eur J Psychol Assess. 2019;35(4):577–91.
Gusy B, Lesener T, Wolter C. Measuring well-being with the Utrecht work engagement scale – student form. Eur J Health Psychol. 2019;26(2):31–8.
Fu Y-c. Measuring personal networks with daily contacts: a single-item survey question and the contact diary. Soc Networks. 2005;27(3):169–86.
Mokgele KRF. Study demands, study resources and well-being of first year students in south African higher education institutions. Vaal Triangle Campus: North-West University; 2014.
van Zyl LE, Rothmann S, Zondervan-Zwijnenburg MAJ. Longitudinal trajectories of study characteristics and mental health before and during the COVID-19 lockdown. Front Psychol. 2021;12:633533.
Corp I. IBM SPSS statistics for windows, version 27.0. In. IBM Corp: Armonk; 2020.
Hayes AF, Little TD. Introduction to mediation, moderation, and conditional process analysis : a regression-based approach. 2nd ed. New York: The Guilford Press; 2018.
Ziegelstein RC. Creating structured opportunities for social engagement to promote well-being and avoid burnout in medical students and residents. Acad Med. 2018;93(4):537–9.
Barrow B, Meiman J, Davidson A, Simms T. Near-peer mentoring in medical school: support for a group model. Med Educ. 2021;55(11):1325–6.
Moir F, Henning M, Hassed C, Moyes SA, Elley CR. A peer-support and mindfulness program to improve the mental health of medical students. Teach Lear Med. 2016;28(3):293–302.
Abrams MP, Daly KD, Suprun A. Peer support expands wellness services and reduces mental health stigma. Med Educ. 2020;54(11):1050–1.
Ahmed A-K, Nault T, Rizos J, Taneja K, Kim GP. Peer support: a medical student-driven mental health workshop. Med Educ. 2020;54(5):469–70.
Pek Z, Zimmerberg-Helms J, Barrett E, Combs S. Resident-led virtual peer-support conferences during the COVID-19 pandemic. Med Educ. 2021;55(11):1305–6.
Winer ES, Cervone D, Bryant J, McKinney C, Liu RT, Nadorff MR. Distinguishing mediational models and analyses in clinical psychology: Atemporal associations do not imply causation. J Clin Psychol. 2016;72(9):947–55.
Hu PJ-H, Hui W. Examining the role of learning engagement in technology-mediated learning and its effects on learning effectiveness and satisfaction. Decis Support Syst. 2012;53(4):782–92.
Luo Y, Xie M, Lian Z. Emotional engagement and student satisfaction: a study of Chinese college students based on a nationally representative sample. Asia Pac Educ Res. 2019;28(4):283–92.
Orgambídez-Ramos A, de Almeida H. Work engagement, social support, and job satisfaction in Portuguese nursing staff: a winning combination. Appl Nurs Res. 2017;36:37–41.
Kauffman H. A review of predictive factors of student success in and satisfaction with online learning. Res Learn Technol. 2015;23:26507.
Richardson JC, Maeda Y, Lv J, Caskurlu S. Social presence in relation to students' satisfaction and learning in the online environment: a meta-analysis. Comput Hum Behav. 2017;71:402–17.
Spanjers IAE, Könings KD, Leppink J, Verstegen DML, de Jong N, Czabanowska K, et al. The promised land of blended learning: quizzes as a moderator. Educ Res Rev. 2015;15:59–74.
Aantal wo-ingeschrevenen (binnen domein ho) [https://duo.nl/open_onderwijsdata/databestanden/ho/ingeschreven/ingeschrevenen-wo1.jsp].
Hilverda F, Vollmann M, Scheepers RA, Nieboer AP, Wissing RO. Peer relations buffer the negative effects of forced online education in medical students dataset. EUR Data Repository. 2022. https://doi.org/10.25397/eur.18257165.
We thank the student organization De Geneeskundestudent, the foundation Stichting MEgala, and Rixt Bruinsma for their help with data collection.
This research was partly supported by a grant provided by the Netherlands Organisation for Health Research and Development (ZonMw), Grant No: 10430 03201 0023.
Ethics approval and consent to participate
The study followed the principles of the Declaration of Helsinki and participants were treated according to the American Psychological Association (APA) ethical standards. The study has been approved by the Medical Ethics Review Committee of the Erasmus Medical Center (#2020–0815, 3-11-2020). Informed consent was obtained from all participants involved in the study.
Consent for publication
Not applicable, since this study does not contain any identifiable individual data and images.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Wissing, R.O., Hilverda, F., Scheepers, R.A. et al. Peer relationships buffer the negative association of online education with education satisfaction and subsequently with study engagement among undergraduate medical students. BMC Med Educ 22, 276 (2022). https://doi.org/10.1186/s12909-022-03337-3