Introduction
Professional identity formation (PIF) and the development of professionalism among medical students constitute a continuous process, which is achieved gradually through the interaction between medical students and their learning environment. Research indicates that medical students who exhibit high commitment to professional values tend to experience less fatigue, ambiguity, and emotional stress, which can positively impact their performance [1-3]. Medical school is a long-term, continuous process with various internal and external challenges. This process will affect the PIF of medical students [4].
Non-academic activities (NAA) represent an external factor that influences the development of professionalism and PIF among medical students. However, studies have indicated that only 83.5% of medical students engage in such activities, notwithstanding that student organizations are recognized opportunities for medical students to interact with peers, thereby influencing the process of PIF. In their systematic review, Sarraf-Yazdi et al. [5] classified informal curriculum activities undertaken by medical schools as one of the strategies supporting PIF. This informal curriculum provides a platform for informal interaction within a community comprising fellow medical students, seniors, or role models, and opportunities to engage with patients in community service activities, thereby enhancing student learning and reinforcing values and behaviors requisite of medical students [6,7].
Previous studies have primarily examined the positive correlation between students’ NAA and their ability to overcome difficult situations (“adversity quotient”) [1]. However, there remains a scarcity of studies exploring the relationship between NAA and PIF among medical students. Hence, this study explores the association between involvement in NAA and the stages of PIF among medical students.
Methods
1. Ethics statement
This research has been approved by the Ethics Committee of the Faculty of Medicine, Universitas Indonesia, with registration number KET-665/UN2.F1/ETIK/PPM. 00.02/2023. All participants provided informed consent prior to their involvement in the study.
2. Study design
This was an observational, cross-sectional study reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement [8].
3. Setting
A multicenter approach spanning 50 Indonesian medical schools was employed with assistants in each center to encourage survey distribution. Data collection occurred within the period of August 10th, 2023, to February 2nd, 2024. All data were self-reported through Google Forms (Google LLC, Mountain View, USA), which facilitated wide-reaching access and ease of response across the diverse geographic locations of the participating medical institutions.
Indonesia’s medical education system slightly differs from that of other countries. In Indonesia, medical students can go straight to university after graduating from high school and study medicine as undergraduate students for at least 3.5 years, earning the title of Bachelor of Medicine. Afterward, they must complete a 2-year clinical phase to obtain the title of doctor [9]. In contrast, in the United States, students must first complete a 4-year bachelor’s program before proceeding to medical school, which lasts another 4 years [10]. Consequently, Indonesian medical students mainly comprise of young adolescents aged 17–25, a period of young adulthood substantial to personal development [11].
4. Participants
The participation criteria include (1) medical students in pre-clinical years (Years 2–4), or clinical years (Years 5–6); and (2) willingness to participate in the study. Meanwhile, the exclusion criteria were (1) students having taken a leave of absence or repeated a year of study; and (2) unverifiable rationalization for total reported hours of NAA per week above 50.
5. Variables
Hours of NAA refers to self-reported hours spent participating in various types of NAA. PIF denotes the process of cultivating professional attitudes and essential components within a competency-based educational framework.
6. Data sources/measurement
We administered the validated 7-Likert-scale Indonesian version of the Developmental Scale (DS) questionnaire, which measures PIF stages; this scale has a reliable Cronbach α, both in literature (0.776) [2] and in our dataset (0.746). To measure NAA, respondents were prompted to categorize their NAA into nine domains: (1) leadership roles, (2) research endeavors, (3) community service engagements, (4) teaching assistant responsibilities, (5) personal hobbies, (6) extracurricular learning, (7) competition-related activities, (8) involvement in religious activities, and (9) paid part-time work. Such categorization facilitated a nuanced understanding of the diverse extracurricular engagements prevalent within various student organizations. Students were asked to provide an estimation of hours spent per week in each NAA domain during the past year. Survey questionnaires are available in Supplement 1.
7. Bias
Considering that our questionnaire is self-reported and demands subjects to recall their weekly average activities in the past year, recall bias is inevitable. As such, participants were encouraged to recheck their calendars and list their activities to rationalize the numbers, which were then checked in the data-cleaning process. Selection bias was also likely to occur, again because the data collection process was voluntary.
8. Study size
This study employed simple random sampling, with the minimum sample size determined using criteria for cross-sectional studies. As the focus was on proportions, the sample size was calculated for categorical variables [12,13]. With a margin error of 10% and a confidence interval of 95%, a minimum of 96 participants were required for this study. To anticipate a low response rate from randomization results, and assuming a response rate of 50%, a minimum of 192 respondents from all centers in Indonesia will be randomly selected and contacted.
9. Statistical methods
Responses from Google Forms were cleaned and coded in Google Sheets (Google LLC). The dataset was then analyzed using IBM SPSS ver. 23.0 (IBM Corp., Armonk, USA). Dichotomous and categorical data were presented in frequencies and percentages. Meanwhile, Kolmogorov-Smirnov normality tests were conducted on continuous data because they are presented in mean±standard deviation or median (minimum to maximum range). Total hours of NAA and PIF scores were stratified according to sex, year of study, university region, and institution type (public/private). The Mann-Whitney U test was used to determine differences between sex and institution type, while an analysis of variance (ANOVA) test was employed to identify differences between years of study and geographical regions, depending on the data normality distribution. Appropriate post hoc tests were conducted for significant ANOVA results. Similarly, Spearman’s rho was utilized to evaluate the correlation between NAA hours and DS. Lastly, multiple linear regression was conducted, with sociodemographic characteristics and hours for each NAA category as predictors of PIF. All analyses were conducted with a significance level of 0.05.
Results
1. Participants
Surveys were sent out to 2,029 Indonesian medical students. Of the 1,234 students who filled out the survey (60.82% response rate), 83 responses were excluded due to ineligibility, and 21 due to incomplete data. After excluding 192 responses through computer randomization to prevent data imbalance from one institution, 938 responses—encompassing 49 institutions—were included in the analysis. The respondents’ demographic characteristics are detailed in Table 1.
2. Main results
Overall, Indonesian medical students spend a median of 13 hours a week on NAA, with a median PIF score of 5.07, on a scale of 7. NAA hours were significantly different across sex groups, years of study, university regions, and institution types (Table 1). Female participants passed significantly more NAA hours (median, 13.5 hr/wk; range, 0–71 hr/wk) than male participants (median, 12 hr/wk; range, 0–89 hr/wk). Post hoc analysis using Mann-Whitney U tests revealed differences to be only significant between Years 2–4 and Year 6; the latter spending more hours on NAA (Table 2).
Over half of participants reported spending time on hobbies (82.8%), leadership activities (80%), or community service activities (62.6%) (Fig. 1A). Moreover, because a Kruskal-Wallis test was conducted per NAA category, we discovered significant differences between years of study for community service (p=0.041), teaching assistance (p=0.012), extracurricular learning (p=0.001), and paid part-time work (p<0.001). Between years of study, post hoc analysis found that lower-year students spend significantly more time on community service activities; Year 2 spends more hours compared to Years 4 and 5, as well as Year 3 over Years 4 and 5. Teaching assistance activities also displayed significant differences, with Years 2 and 3 spending more time than Year 4. However, Year 4 exhibited a lower amount of time compared to Years 5 and 6. Time spent on extracurricular learning activities was markedly higher in Year 4 than in Year 2; Year 5 than in Years 2, 3, and 4; and in Year 6 than in Years 2, 3, and 4 (Table 2). Complete post hoc results are presented in Table 2.
Because we identified significant (p<0.001) variability in NAA hours between Indonesian Medical Education Association (IMEA) regions (Fig. 1B), we discovered that Regions 5 and 6 both spent significantly more hours on NAA as compared to Regions 1, 2, and 3. Furthermore, we found NAA hours between IMEA regions to be significantly different for all categories (p<0.001) except for paid part-time work. Post hoc analysis revealed significant variations among specific regions and activity categories. Region 6 is the most active in NAA, which surpasses all other regions in total hours, with significant results in several categories. These results reveal varied activity patterns among IMEA regions (Table 3).
3. Correlation and predictors of PIF scores
We found a very low correlation (Spearman’s rho=0.138, p<0.001) between total NAA hours and PIF scores. Nevertheless, from our multiple regression analysis, we yielded a model with a multiple correlation coefficient (R) of 0.196 (F [13, 924]=2.824, p=0.001, R2=0.038) (Table 4). We discovered that for each hour spent on research and competition-related activities, PIF scores were significantly predicted to increase by 0.021 and 0.015, respectively.
Discussion
This study explores the association between students’ engagement in NAA and the stages of PIF. Overall findings of this study showed that students spent a substantial number of hours on various NAA and displayed differences between medical students’ PIF scores. Research and competition-related activities were found to be predictors of PIF scores.
There were significant differences in NAA hours across sex, year of study, university regions, and public and private institutions. A shift in categories of activities students engaged with NAA was found across study years: earlier pre-clinical year students favored community service and teaching assistance activities, while later clinical year students preferred extracurricular learning and paid part-time work. A significant difference was also found in activity categories between IMEA regions.
The results of this study showed that students’ PIF stages are not significantly different across years and regions, which was contradictory to the results from a previous study in one institution in Indonesia, which showed that more advanced groups exhibited higher PIF scores [2]. The discrepancy could be due to the different periods of study. The previous study was conducted during the coronavirus disease 2019 pandemic, while this study was conducted after it, which caused significant disruptions to student interactions, socialization, and the opportunity to engage in activities that are central to their PIF [14].
Our multicenter study addressed these contextual changes by examining PIF stages across different years and regions, which provided insight into shifts in interaction patterns during and after the pandemic. Despite these observations, it is important to recognize that PIF is a dynamic process [2]. The cross-sectional nature of our study and the median PIF score of 5 should be viewed as indicative of current attributes, rather than definitive stages. The original questionnaire by Tagawa [15] does not offer specific benchmarks for interpreting these scores but serves as a framework for evaluating professional identity attributes.
To understand the observed variations in NAA hours and PIF scores, Self-Determination Theory (SDT) might serve as a valuable framework. According to SDT, intrinsic motivation—which is driven by a sense of autonomy, competence, and relatedness—is crucial for effective learning and development. The significant differences in NAA hours across various years of study may reflect a growing need for challenges and autonomy as students progress through their education, which can be seen in the shift from community service and teaching assistance as the dominant NAA in earlier years, to paid part-time work in later years. This could be seen as a response to increased motivation and a desire for more meaningful and challenging experiences, which leads to enhanced PIF because early-years medical students might favor activities that provide initial clinical experience and community care [16]. It is also consistent with how adult learners are motivated by goals and objectives relevant to their personal and professional lives [17]. As students advance in their medical education, their focus shifts toward activities that align more closely with their career aspirations and professional development [16,18]. The increased involvement in research and competitionrelated activities in later years is indicative of this shift, which highlights the students’ growing focus on activities that directly contribute to their PIF [18].
The common PIF and NAA findings across regions, may be explained by the different regions of which medical schools were located, which affect the resources of the medical schools. Nevertheless, the standardized outcomebased education approach was implemented in all Indonesian medical schools through application of the 2012 Indonesian Standard for Doctor Competency [19]. Nonetheless, all medical schools are encouraged to adapt their curricula accommodating the specific contexts, the health problems within their regions, as well as the resources of the medical schools. These adaptations also influence the design and execution of NAA programs undertaken by medical students in each school [20].
Furthermore, the profile of “Generation Z” medical students, characterized by high competitiveness and a strong drive for personal and professional achievement, aligns with the findings of Zhuhra et al. [21]. This generation’s preference for competitive and researchoriented activities reflects their desire for recognition and career advancement [21]. Supporting this, Vaa Stelling et al. [22] found that early-career faculty physicians experience similar tensions, in that they strive to both fit in and stand out, which often leads to imposter syndrome and burnout. This highlights the importance of engaging students in reflective dialogues and finding purposes to mitigate these challenges and support PIF, while still leveraging this trait by encouraging engagement in research and competition-related activities. Tailoring support through coaching and mentoring to align with the motivations and career goals of Generation Z students can enhance their overall development and better prepare them for their future medical careers [21,22].
Results also showed that public medical schools have higher rates of hours spent on NAA activities compared to private schools. Such a phenomenon was in line with the study by Lazarus et al. [23] discovering more prior health volunteering experience in state-university students, although findings in Brazil found the opposite trend [24]. In this study, the differences may be attributed to public and private institutions being unevenly distributed across regions in Indonesia. The phenomenon could also be explained by the disparity in opportunities to be engaged in different types of NAA. Therefore, collaborations and integration efforts (e.g., resource sharing and co-creation activities, and so forth) among medical students across different regions could enhance their engagement in NAA, which could be facilitated through engagement in national student organizations.
The results of our study are consistent with the study by Achar Fujii et al. [25], which assessed the participation of medical students in community-based NAA (CBNAA). According to this experience, CBNAA contributed to students’ PIF by positively impacting students’ acquisition of new knowledge, sharpening their clinical reasoning, and helping them to develop the professional attributes of medical doctors (e.g., time management skills, teamwork, responsibility, and empathy). However, the findings also showed that there is a lack of support for these activities, which led to students being reluctant to participate in them. It highlights the importance of institutional support from medical schools for students engaging in NAA [25].
Owing to its self-reported nature, our survey on NAA hours was prone to recall bias, in terms of both underestimation and overestimation. In addition, because participation in the study was voluntary, there were also significantly more males, more private medical students, and fewer participants from particular regions in the participants’ demographics. This study is especially generalizable in the Indonesian context because nationwide institutions were represented. NAA in different countries may vary, but as medical students adopt similar routines, their role in PIF might be similar. However, the results of this study, which involves students from different centers across Indonesia, with different resources and learning environments, may provide a snapshot of dynamic participation in NAA across various contexts, and its impact on students’ PIF.
Further exploration of how NAA promotes PIF should be conducted, particularly in contexts like Indonesia, where collectivist cultures and heavy student involvement in NAA are prominent. Previous studies explored professionalism but were limited to extracurricular activities exposing medical students to certain fields [26]. Furthermore, as this cross-sectional study could not determine causation, future longitudinal studies tracking medical school alumni could provide insights into how the intensity and type of NAA engagement influence career trajectories and professional growth. Although several NAA activities do not directly predict PIF scores, others— such as sports, hobbies, and religion—should also be involved in further studies because they may serve as healthy coping mechanisms, facilitating students’ wellbeing [27]. Comparative studies in collectivist contexts could identify universal and cultural-specific elements of NAA that support PIF, enabling medical schools to optimize NAA’s potential in medical education.
In conclusion, NAA are positively associated with students’ PIF process, with research and competitionrelated activities serving as predictors. Supporting these NAA becomes imperative for medical schools in order to optimize student potential, motivation, and PIF.
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