1. Introduction
Non-medical use of prescription stimulants, such as methylphenidate and amphetamine-based medications, has significantly increased among university students worldwide, raising concerns among health professionals and educators (Arria et al., 2018; Benson et al., 2021; McCabe et al., 2017). Stimulants are primarily prescribed to treat attention deficit hyperactivity disorder (ADHD), enhancing cognitive performance by increasing extracellular dopamine and norepinephrine levels in the brain (Kolar et al., 2008). While these medications are effective for individuals with ADHD, a growing number of students without a formal diagnosis use them to enhance their focus, concentration, and academic performance, especially during high-stress periods such as examinations (Kennedy, 2018; Sattler & Wiegel, 2013). Despite a growing body of research, significant gaps remain regarding the psychological and social predictors of stimulant use among students, particularly in terms of self-efficacy, attitudes, and peer pressure.
Studies indicate that between 6% to 25% of college students in the U.S. have engaged in non-medical stimulant use (Compton et al., 2018; Herman et al., 2011), with similar trends observed in Europe (Helmer et al., 2016; Sabbe et al., 2022) and Israel (Bonny-Noach & Ne’eman-Haviv, 2018; Korn et al., 2019). The motivations for non-prescribed stimulant use are often academic, such as enhancing study efficiency or staying awake longer during exam periods (Teter et al., 2006; Herman et al., 2011). However, non-academic motives like social enhancement or appetite suppression have also been documented (Giordano et al., 2015; Korn et al., 2019).
Attitudes toward stimulant use play a significant role in predicting actual behavior (Ponnet et al., 2015; Helmer et al., 2016). Research suggests that students who hold more permissive or positive views toward stimulant use are more likely to engage in such behavior, even if they are aware of the potential risks (Ponnet et al., 2021). Erasmus and Kotzé (2020) found that favorable attitudes toward pharmacological cognitive enhancements predict an increased likelihood of stimulant use among medical students. Similarly, Shohani et al. (2021) identified that positive perceptions and curiosity toward stimulants significantly influence stimulant use. Such attitudes are influenced by peer norms, misconceptions regarding stimulants’ safety, and the perceived prevalence of their use among fellow students (Verdi et al., 2016; Helmer et al., 2016). Nevertheless, these studies lack depth in exploring the psychological mechanisms that link attitudes to behavior, leaving open the question of whether attitudes exert a direct influence on usage or mediate the effects of other psychosocial factors.
In addition to attitudes, psychological factors such as self-efficacy are critical in understanding stimulant use. Self-efficacy refers to an individual’s belief in their ability to execute tasks and achieve goals (Bandura, 1997) and has emerged as a potential protective factor against non-prescribed substance use. Prior studies have found a negative correlation between self-efficacy and substance use, including the misuse of prescription stimulants (Kadden & Litt, 2011; Alavi, 2011). Students with lower levels of self-efficacy may resort to stimulants as a coping mechanism to compensate for perceived academic inadequacies (Ford & Ong, 2014; Glass et al., 2011). Chen et al. (2001) conceptualized general self-efficacy as crucial for academic resilience and coping strategies. However, the existing literature fails to examine whether self-efficacy directly influences stimulant use or operates indirectly by shaping attitudes toward stimulants.
Despite the growing body of research on non-prescribed stimulant use, much of the literature originates from the United States, with relatively limited data available from other cultural and educational contexts, including Israel. Furthermore, while previous studies have examined the prevalence and motivations for stimulant use, few have specifically explored the psychological mechanisms underlying this behavior, such as self-efficacy. In particular, the Israeli academic context has limited comprehensive current research that integrates both attitudinal and psychological factors in explaining stimulant use. This study aims to address this research gap by investigating how students’ self-efficacy levels and their attitudes toward stimulants interact to predict actual use.
To enrich our theoretical framework and explain the mechanisms underlying stimulant use better, we draw on established psychological models of stress, coping, and behavioral intention. College students’ decision to use cognitive-enhancing stimulants can be conceptualized within the Transactional Model of Stress and Coping (Lazarus & Folkman, 1984). According to this model, academic demands are appraised as threats or challenges; when perceived coping resources (e.g., self-efficacy) are insufficient, students may adopt maladaptive coping strategies, such as non-medical stimulant use, to restore a sense of control. This process is reinforced by positive outcome expectancies and perceived social norms, consistent with the Theory of Planned Behavior (Ajzen, 1991) and the Self-Medication Hypothesis for substance use. Consequently, self-efficacy not only exerts a direct protective effect but also shapes cognitive appraisals (e.g., the perceived benefits vs. risks), which in turn influence attitudes and behavior.
The current study aims to examine the relationships between self-efficacy, attitudes toward stimulant use, and actual stimulant consumption among college students in Israel. It proposes an integrative model in which self-efficacy predicts the non-medical use of prescription stimulants through attitudinal mediation, with demographic variables considered as potential moderators. Based on the literature, the following hypotheses are proposed: (1) Students with more favorable attitudes toward stimulant use will be more likely to engage in stimulant use. (2) Higher self-efficacy will be associated with negative attitudes toward stimulant use and lower use of stimulants. (3) Higher self-efficacy will be indirectly associated with lower stimulant use through its negative influence on favorable attitudes toward stimulant use (the mediation hypothesis). Clarifying these relationships will deepen our theoretical understanding and inform targeted interventions aimed at reducing non-prescribed stimulant use among university students.
2. Materials and Methods
2.1. The Research Procedure
A cross-sectional online survey was conducted among students at Ashkelon Academic College (southern Israel) and the Max Stern Yezreel Valley College (northern Israel). This study included students enrolled in various academic programs at both colleges. Inclusion criteria: Enrolment at either college and the provision of informed consent. Exclusion criteria: Exchange students, incomplete questionnaires, or withdrawal before completion. The survey was administered between January and February 2025 at Ashkelon Academic College and between February and March 2025 at the Max Stern Yezreel Valley College. Ethical approval was obtained from the Ashkelon Academic College Ethics Committee (approval #52-2024) and the Max Stern Yezreel Valley College Ethics Committee (approval #YVC EMEK 2025-22).
The online survey was developed using Qualtrics software (Qualtrics, Provo, UT, USA) and distributed to all students via email, with a follow-up reminder sent a month later. A total of 876 students participated from both colleges, of whom 598 completed the questionnaire in full, yielding a completion rate of 68%. The introductory page of the questionnaire outlined the study’s objectives and emphasized that all responses would remain anonymous. Participation was entirely voluntary, and respondents could discontinue participation at any time without penalty, with no obligation to answer specific questions. Informed consent was obtained electronically from all participants before they proceeded to complete the survey.
2.2. Measures
An online, closed, anonymous, self-completed questionnaire was used (See Appendix A). The questionnaire underwent content validation by an expert in neurology and an expert in health behavior. The questionnaire comprised the following components:
Demographic Information
The participants reported demographic data including their gender, age, marital status, religion, department, prior diagnosis of ADHD, and grade point average (GPA).
Stimulant Use Patterns
Stimulant use patterns were assessed using a questionnaire adapted from Sawchik et al. (2020), translated into Hebrew by a professional translator. The participants reported whether they used stimulants (e.g., Ritalin, Concerta, Adderall, Attent). Those who responded affirmatively then answered additional questions about the age of first use, frequency of use, experiences of negative or positive effects, and ways to obtain stimulants. Non-medical use was defined as the consumption of prescription stimulants without a current, personally issued prescription.
Motivations for Stimulant Use
Participants indicated their reasons for using stimulants, or, if they did not use stimulants, the reasons they believed that other students did. The response options (multiple selections allowed) were adapted from Sawchik et al. (2020) and included ADHD symptoms, improving concentration, enhancing academic performance, staying awake, improving retention of course material, peer influence, escaping reality, weight loss, enhancing athletic performance, and other reasons.
General Self-Efficacy
General self-efficacy was measured using seven items from Chen et al. (2001). The Hebrew version, translated by Tevet-Tubul (2011), demonstrated good internal consistency (α = 0.79). The participants rated their agreement with each statement on a 5-point scale (1 = strongly disagree to 5 = strongly agree). A sample item is “I can achieve most of the goals I set for myself.” The Scores were averaged, with higher scores reflecting greater self-efficacy (α = 0.74 in the current study).
Attitudes Toward Stimulant Use
Attitudes were assessed using eight items adapted from Erasmus and Kotzé (2020), translated into Hebrew by a professional translator. The participants rated their agreement on a 5-point scale (1 = strongly disagree to 5 = strongly agree). A sample item is “It is acceptable to use stimulants to improve concentration while studying for exams.” The responses were averaged, with item 4 reverse-coded. Higher scores reflected more favorable attitudes toward stimulant use. The internal consistency in the current study was high (α = 0.92).
2.3. Data Analysis
Data were analyzed using SPSS 29.0 (IBM, Armonk, NY, USA). In accordance with the policy communicated by the institutions’ administrations, the data analysis in this study was conducted in aggregate form, without distinction between the colleges, in order to preserve institutional anonymity and avoid interpretations that might suggest inter-institutional comparisons.
Descriptive statistics were calculated to summarize the sample characteristics and key study variables. Chi-square tests were used to examine group differences in stimulant use based on demographic characteristics (e.g., religion, smoking status, gender, marital status, and faculty affiliation). To examine the associations between attitudes toward stimulant use, self-efficacy, and actual stimulant consumption, logistic regression analyses were conducted, given that stimulant use was coded as a binary variable (0 = no use, 1 = use). The strength of these associations was reported using odds ratios (Exp(B)) with corresponding p-values. Additionally, a Pearson’s correlation analysis was conducted to assess the relationship between self-efficacy and attitudes toward stimulant use.
To examine whether attitudes toward stimulants mediate the relationship between self-efficacy and stimulant use, a mediation analysis was conducted using Hayes’ PROCESS macro for SPSS (Hayes, 2018; version 4.2, 2022). This analysis tested the indirect effect of self-efficacy on stimulant use through attitudes toward stimulants.
The mediation model examined three key pathways: 1. the effect of self-efficacy on attitudes toward stimulants (path a), 2. the effect of attitudes on stimulant use while controlling for self-efficacy (path b), and 3. the direct effect of self-efficacy on stimulant use while controlling for attitudes (path c). The indirect effect was calculated as the product of paths a and b.
Given that stimulant use was coded as a binary variable, logistic regression was employed for the analyses involving this outcome variable. Bootstrap confidence intervals (95% CIs) with 5000 bootstrap samples were used to test the significance of the indirect effect. Mediation was considered significant if the bootstrap confidence interval for the indirect effect did not include zero. All statistical tests were two-tailed, with a significance level set at p < 0.05.
3. Results
3.1. The Sample Characteristics
In total, 598 students participated in this study, of whom 23% were men, 77% were women, and 82% were Jewish. About half of the students studied in the Faculty of Health Sciences (44%), 40% in Social Sciences, and 16% in Computer Science and Management. A total of 28% were diagnosed with ADHD, and 26% were smokers. Their mean age was 27.30 ± 7.30 years. The participants reported a mean cumulative academic average of 86.20 (SD = 6.46) at the time of the study. The sample characteristics are summarized in Table 1.
3.2. The Use of Stimulants, Reasons, and Side Effects
Approximately two-thirds of the students (n = 371, 62%) reported knowing anywhere from 1 to 40 other students who used stimulants. Approximately one-fifth of the participants (n = 130, 22%) reported using stimulants themselves, with the average age of onset being 20 years (SD = 7.43).
Among stimulant users, 78% had previously been diagnosed with ADHD. About one-fifth reported daily use (n = 28, 22%), 41% used stimulants during busy semester periods (n = 53), and 38% used them only during exam periods (n = 49). The majority of users reported obtaining stimulants through a physician’s prescription (n = 108, 83%). Additional sources included friends (n = 22, 17%), online purchases (2%), and the use of a child’s prescription (2%). Since multiple sources could be selected, the total percentages may exceed 100%.
The participants were asked to indicate their reasons for using stimulants, or, if they were non-users, the reasons they believe other students use them. Multiple responses were allowed. The most frequently reported reason was to improve concentration while studying (75%). This was followed by the presence of ADHD (57%), enhancing academic performance (55%), staying awake or studying for longer hours (33%), improving memorization (26%), appetite suppression or weight loss (10%), escapism or detachment from reality (7%), peer influence (“because everyone is using them”) (6%), and improving athletic performance (5%).
Most stimulant users (n = 105, 81%) reported experiencing positive effects, such as improved concentration and focus, increased alertness, the ability to study for extended hours, and greater ease and success in passing exams. However, 111 users (85% of users) also reported negative experiences related to stimulant use. The most commonly reported side effects were loss of appetite (69% of users), increased heart rate (52% of users), sleep disturbances (46% of users), and anxiety (27% of users). Other reported issues included irritability or mood instability (5%), body tremors (5%), and headaches and migraines (3%). Since multiple sources could be selected, the total percentages may exceed 100%.
A chi-square test revealed that students who used stimulants reported significantly more sleep disturbances compared to those in non-users (53% vs. 37%, respectively; χ2 = 10.96, p < 0.001). Moreover, Jewish participants reported higher usage rates than those in non-Jewish participants (25% vs. 11%, respectively; χ2 = 3.73, p < 0.05). Additionally, smokers reported higher stimulant use compared to that in non-smokers (36% vs. 20%, respectively; χ2 = 12.53, p < 0.001). No significant differences were found in stimulant use based on gender, marital status, parenthood, or faculty affiliation, as these results were not statistically significant (p > 0.05).
3.3. Attitudes Toward Stimulant Use
Table 2 presents the distribution of the responses to items assessing attitudes toward stimulant use. For analytical purposes, the response categories were collapsed as follows: responses 1 and 2 were grouped as “agree to a small extent”, response 3 remained “moderately agree”, and responses 4 and 5 were grouped as “agree to a great extent.”
To construct the variable “Attitudes toward stimulant use”, a mean score was calculated for each participant based on all attitude items. The scale ranged from 1 to 5. The overall mean score was 2.57 (SD = 0.69).
A subgroup analysis examined whether attitudes differed by ADHD diagnosis. Students with ADHD (M = 2.76, SD = 0.71) reported significantly more favorable attitudes toward stimulant use than those in students without ADHD (M = 2.76 ± 0.71 vs. M = 2.49 ± 0.67, respectively, t(566) = 4.25, p < 0.001).
3.4. Self-Efficacy
Table 3 presents the distribution of the responses to items assessing self-efficacy. For analysis purposes, the response categories were collapsed as follows: responses 1 and 2 were grouped as “agree to a small extent”, response 3 was retained as “moderately agree”, and responses 4 and 5 were grouped as “agree to a great extent.”
To construct the variable “Self-Efficacy”, a mean score was calculated for each participant across all relevant items. The possible range of scores was 1 to 5. The overall mean was 3.97 (SD = 0.68).
3.5. The Relationships Between Study Variables
The relationships between attitudes toward stimulant use, self-efficacy, and actual stimulant consumption were tested using logistic regressions. The results indicated that more favorable attitudes toward stimulant use increased the likelihood of the actual use by 231% (Exp(B) = 3.31, p < 0.001). The model was found to be statistically significant (p < 0.001) and explained 15% of the variance in stimulant use. In other words, the more positive attitudes are, the higher the probability of stimulant use.
Moreover, we found that higher levels of self-efficacy reduced the likelihood of stimulant use by 38% (Exp(B) = 0.69, p < 0.01). The model was statistically significant (p < 0.001) and accounted for 2% of the variance in stimulant use. This indicates that the greater one’s sense of self-efficacy, the lower the probability of using stimulants. Hence, this hypothesis was also supported.
Furthermore, the analysis revealed a significant negative correlation between self-efficacy and attitudes toward stimulant use (rp = −0.17, p < 0.001). In other words, higher self-efficacy was associated with more negative attitudes toward the use of stimulants.
3.6. Mediation
A mediation analysis was conducted to examine whether attitudes toward stimulants mediated the relationship between self-efficacy and stimulant use. The results revealed that higher self-efficacy significantly predicted less favorable attitudes toward stimulants (β = −0.146, p < 0.001). In turn, more favorable attitudes significantly increased the likelihood of stimulant use (β = 1.207, p < 0.001). The direct effect of self-efficacy on stimulant use was not significant (β = −0.008, p = 0.958). However, the indirect effect through attitudes was significant (β = −0.176, 95% CI [−0.317, −0.059]), indicating complete mediation. These findings suggest that self-efficacy influences stimulant use entirely through its effect on attitudes toward stimulant use.
4. Discussion
The current study indicates that 22% of students use stimulants, with 17% of these users doing so without a prescription, obtaining substances through online sources or from acquaintances. This finding aligns with a previous study showing that 19.3% of Israeli students reported stimulant use, and 7.2% used them without a prescription (Bonny-Noach & Ne’eman-Haviv, 2018). Similarly, Korn et al. (2019) found that 17% of students reported stimulant use, with 26% of them indicating non-prescribed use of stimulants. This relatively high prevalence may be explained by increased access to these substances. More students diagnosed with ADHD are enrolled in higher education, and stimulants have become more available through social networks, making them more prevalent on campuses.
One key pattern revealed in this study is that about half of stimulant users consume them during peak academic stress, such as exam periods, primarily to enhance focus. Indeed, 75% cited improved concentration as the main reason for their use, reflecting growing global trends of cognitive and performance enhancements through stimulants (Figueroa et al., 2020; Korn et al., 2019). While many users reported perceived academic benefits such as increased alertness and focus, the majority also experienced adverse effects, including appetite loss, rapid heart rates, and sleep disturbances. These side effects are consistent with previous research linking higher dosages to fatigue, irritability, sleep disruption, and reduced concentration (Reske et al., 2015; Zullig et al., 2015).
Overall, the students’ attitudes toward stimulant use were not particularly favorable (M = 2.57 on a 5-point scale). This finding contrasts with previous studies indicating more permissive views. For example, Verdi et al. (2016) found that many students perceived stimulants as accessible and safe, with a quarter stating that occasional prescription stimulant use is harmless. Cultural differences may explain this discrepancy. In some countries, stimulant use is more socially normalized and regarded as a routine means of enhancing performance (Maier et al., 2013; Sattler et al., 2013; Vargo et al., 2014). In contrast, more conservative health and cultural norms in Israel may shape more cautious attitudes.
Interestingly, nearly two-thirds of the respondents (63%) agreed that it is fair for students to use stimulants to enhance their cognitive abilities, suggesting a perceived justification under academic pressure. High performance demands, competition, and fear of failure may drive students to seek quick solutions such as stimulant use. This aligns with the findings from Sailo and Varghese (2024), in which students described the education system as contributing to stress and noted that they might not resort to stimulants if alternative coping tools were available. This perception may also reflect notions of functional fairness, the belief that stimulant use helps level the playing field or ensure equal academic opportunity. In achievement-oriented academic cultures, such use may not be viewed as unethical but rather as a legitimate strategy to meet institutional expectations (Sattler et al., 2013; Vargo et al., 2014). Additionally, market-driven norms framing academic success as a future investment may reinforce the social acceptance of cognitive enhancement further.
This gap suggests that while students generally believe in their abilities, their confidence may decrease when facing more challenging academic demands. This distinction between general self-efficacy and task-specific self-efficacy (Bandura, 1997) is known as the “efficacy gap” (Schunk & Pajares, 2002) and may explain why some students turn to stimulants when encountering complex tasks. Academic pressure may undermine situational confidence, leading them to seek external compensatory mechanisms (Chemers et al., 2001; Zajacova et al., 2005).
We found no significant differences in stimulant use across gender, marital status, parenthood, or faculty. The lack of variation by field of study is consistent with previous findings (Sümbül-Şekerci et al., 2021). However, prior research on gender differences has been mixed. While some studies reported higher use among women (Bonny-Noach & Ne’eman-Haviv, 2018), others found greater use among men, possibly due to the higher prevalence of ADHD in males (Looby et al., 2015; Ponnet et al., 2021). The current study’s lack of a gender difference may reflect shifting gender norms, with women increasingly present in higher education and achieving strong academic outcomes (Alon & Gelbgiser, 2011; Shavit et al., 2008). We also found higher stimulant use among Jewish students compared to non-Jewish peers and among smokers compared to non-smokers. These differences may reflect cultural differences in ADHD awareness and treatment accessibility (Hoshen et al., 2016). Additionally, smoking is often linked to other risk behaviors and psychoactive substance use, potentially serving as a “gateway” to stimulant use (Bonny-Noach & Ne’eman-Haviv, 2018; Weinberger et al., 2017).
This study found that more positive attitudes toward stimulant use were associated with a higher likelihood of their actual use. This finding is consistent with previous research. In Belgium, positive attitudes were linked to a greater intention to use stimulants (Ponnet et al., 2021), and similar results were reported in the U.S., where beliefs about the academic benefits of stimulants predicted non-medical use (Ford & Ong, 2014). In the Netherlands, some students even considered faking an ADHD diagnosis to access stimulants (Fuermaier et al., 2021), and in Turkey, users perceived stimulant use as normative and relatively harmless (Sümbül-Şekerci et al., 2021). These findings can be explained through the Theory of Planned Behavior (Ajzen, 1991), which posits that positive attitudes increase the likelihood of intention and action. Social norms and peer influence (Vargo et al., 2014), along with moral justification mechanisms (Merritt et al., 2010), may further facilitate use, particularly under academic pressure or perceived performance gaps.
Higher self-efficacy was associated with more negative attitudes toward stimulant use. This finding aligns with the existing literature emphasizing the role of self-efficacy in shaping substance-related attitudes and behaviors. For example, Hillman (2017) found that students with low self-efficacy held more favorable views of stimulant use and perceived greater benefits. Similar results were reported among adolescents regarding psychiatric medication (O’Brien et al., 2013) and in an Iranian study linking low self-efficacy to positive attitudes toward psychoactive substances (Allahverdipour et al., 2007). Tate et al. (2008) noted that individuals with high self-efficacy are more likely to trust their own coping abilities and avoid relying on external substances. These results lend support to the Health Belief Model (Rosenstock et al., 1988), which suggests that individuals with a strong sense of personal capability are less likely to perceive stimulant use as necessary or beneficial, leading to more negative attitudes.
Higher levels of self-efficacy were associated with a reduced likelihood of stimulant use. This finding supports Bandura’s (1997) social–cognitive theory, which suggests that individuals with high self-efficacy are better equipped to regulate their behavior, manage stress, and resist harmful temptations. Self-efficacy functions as a key self-regulatory mechanism, promoting impulse control and responsible decision-making. Previous studies support this link. Self-efficacy has been shown to reduce the likelihood of psychoactive substance use (Kadden & Litt, 2011) and protect against addiction risk (Alavi, 2011). Looby et al. (2015) found that high self-efficacy and positive health perceptions correlated with lower rates of stimulant use. Similarly, students who based their self-worth on moral values were less likely to use stimulants for academic enhancement (Giordano et al., 2015), and those with high self-esteem showed lower risk behavior (Tam et al., 2020).
The mediation analysis revealed that self-efficacy influences stimulant use entirely through its effect on attitudes. Specifically, higher self-efficacy was associated with more negative attitudes toward stimulants, which in turn reduced the likelihood of use. This complete mediation suggests that attitudes serve as the primary mechanism through which self-efficacy protects against stimulant use.
Although the present study did not directly assess long-term clinical outcomes, prior research has identified more serious risks associated with non-medical stimulant use. These include the potential for psychological dependence, cardiovascular side effects such as hypertension and tachycardia, and psychiatric complications including anxiety, agitation, and, in rare cases, psychosis (McCabe et al., 2017; Benson et al., 2021). These findings underscore the importance of early intervention and prevention efforts on campuses, even among students who perceive their use as harmless or functional.
Study Limitations
The present study has several limitations. First, the sample was limited to students from two academic institutions, which limits the generalizability of the results to the broader population of students across Israel. Additionally, 77% of the sample consisted of women, which may limit the generalizability of the findings. However, this gender distribution reflects the actual proportion of female students at these colleges. Previous research suggests that gender may influence both the prevalence and motivations for stimulant use, as well as attitudes toward such substances. For example, women may experience greater academic pressure or anxiety, while men may be more likely to receive an ADHD diagnosis and access prescription stimulants. These gender-based differences warrant further investigation in more demographically balanced samples. Second, the data collection relied on self-report measures, which may be subject to social desirability bias or inaccuracies in recalling stimulant use behaviors. Third, this study employed a cross-sectional design, which precludes drawing conclusions about the causal relationships between the variables. Moreover, since the data collection was based on a voluntary online survey, this study may be subject to self-selection bias. Students with prior experience with stimulant use or a particular interest in the topic may have been more inclined to participate, potentially inflating the prevalence estimates or influencing the attitudinal profile of the sample. It is also important to consider the possibility that participants may have underreported their use of stimulants or expressed less favorable attitudes toward such use due to stigma-related concerns. Health-related stigma can lead individuals to conceal information, avoid disclosure, and experience internalized feelings of shame and guilt, factors that may influence both health behavior and social interaction (Dopelt et al., 2024). Finally, other potential variables, such as test anxiety, depression, or perfectionistic tendencies, were not included in the current model and may also influence stimulant use.
5. Conclusions and Recommendations
This study enhances our understanding of stimulant use among Israeli college students, revealing that positive attitudes toward stimulants and low self-efficacy are associated with an increased likelihood of use. Many students reported turning to stimulants during periods of academic stress, particularly when lacking confidence in their academic abilities. These findings underscore the need for institutional interventions aimed at strengthening students’ self-efficacy, increasing their awareness of the risks of stimulant use, and promoting healthier coping strategies. Such initiatives may include study skill workshops, academic counseling, and emotional support services. Future research should broaden the sample to include students from diverse academic and cultural backgrounds and explore additional influencing factors, such as test anxiety, perfectionism, and depression. Investigating protective factors such as social support, emotional regulation, and moral reasoning may further inform the development of effective prevention and intervention programs.
Conceptualization: K.D., S.B. and N.H.-K.; methodology: K.D. and N.H.-K.; software: K.D.; formal analysis: K.D.; data curation: K.D., S.B. and N.H.-K.; writing—original draft preparation: K.D.; writing—review and editing: K.D., S.B. and N.H.-K. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Decla-ration of Helsinki, and approved by the Ashkelon Academic College Ethics Committee (protocol code #52-2024, approved on 16 December 2024) and The Max Stern Yezreel Valley College Ethics Committee (protocol code #YVC EMEK 2025-22, approved on 8 January 2025).
Informed consent was obtained from all participants.
The data presented in this study are available on request from the corresponding author.
The authors declare no conflicts of interest.
Footnotes
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Sample characteristics.
| Characteristics | n | % |
|---|---|---|
| Male | 136 | 23 |
| Female | 362 | 77 |
| In a relationship | 316 | 53 |
| Has children | 149 | 25 |
| Jewish | 492 | 82 |
| Not Jewish | 106 | 18 |
| Faculty: | ||
| Health Sciences | 258 | 44 |
| Social Sciences | 235 | 40 |
| Computer Science and Management | 97 | 16 |
| Diagnosed with ADHD | 170 | 28 |
| Smoker | 156 | 26 |
Distribution of attitudes toward stimulant use.
| Statement | Weakly (%) | Moderately (%) | Strongly (%) |
|---|---|---|---|
| Sometimes students without ADHD use stimulants for cognitive enhancement | 36 | 34 | 30 |
| Stimulants help improve academic performance in individuals without ADHD | 38 | 32 | 30 |
| It is acceptable to use stimulants to improve concentration while studying for exams | 27 | 26 | 47 |
| * Stimulants may be harmful to health | 55 | 30 | 15 |
| It is fair for students to take stimulants to enhance their cognitive abilities | 27 | 28 | 35 |
| I would feel pressured to take stimulants if I knew other students were using them to enhance performance | 81 | 12 | 7 |
| Poor academic performance justifies the use of stimulants | 64 | 21 | 15 |
| Physicians should be allowed to prescribe stimulants for cognitive enhancement in individuals without ADHD | 66 | 20 | 14 |
* Reverse-coded item. Distribution is presented after reverse scoring.
Distribution of self-efficacy items.
| Statement | Weakly (%) | Moderately (%) | Strongly (%) |
|---|---|---|---|
| I can achieve most of the goals I set for myself | 4 | 23 | 73 |
| When facing difficult tasks, I am confident that I can accomplish them | 7 | 27 | 66 |
| In general, I believe I can achieve what matters to me | 4 | 14 | 82 |
| I can succeed in any task when I am determined to do so | 3 | 16 | 81 |
| I believe I can successfully overcome many challenges | 5 | 13 | 82 |
| I am confident in my ability to perform most tasks well | 4 | 15 | 81 |
| Compared to others, I can perform most tasks effectively | 6 | 24 | 70 |
Appendix A. Research Questionnaire
1. Gender:
☐ Male
☐ Female
☐ Prefer not to answer
2. Age: ___
3. Are you currently in a romantic relationship?
☐ Yes
☐ No
4. Do you have children?
☐ Yes
☐ No
☐ I am currently pregnant
5. What is your religion?
☐ Jewish
☐ Muslim
☐ Christian
☐ Other: ____
6. Academic Program:
☐ Bachelor’s degree
☐ Master’s degree
☐ Social Work retraining
☐ Nursing retraining
☐ Practical Engineering School
☐ Preparatory (Mechina) Program
7. Department: ___
8. Year of Study:
☐ Year 1
☐ Year 2
☐ Year 3
☐ Year 4
9. Have you ever been diagnosed with ADHD?
☐ Yes
☐ No
10. What is your current GPA (grade point average)? ___
11. Do you smoke?
☐ No
☐ Yes
☐ Yes, only socially
☐ I used to smoke but quit
12. On average, how many hours per day do you use social media (TikTok, Instagram, Telegram, Facebook, but not WhatsApp)? __
Stimulant Use Patterns
1. Do you use stimulant substances (e.g., Ritalin, Concerta, Adderall, Attent)?
☐ Yes
☐ No
If yes:
2. At what age did you first use stimulants? _______
3. How frequently do you use stimulants?
☐ Daily
☐ Only during exam periods
☐ During stressful periods of the semester
4. Have you experienced any negative effects after using stimulants? * (Check all that apply) *
☐ No negative effects
☐ Rapid heartbeat
☐ Headaches
☐ Reduced appetite
☐ Sleep disturbances
☐ Anxiety
☐ Other: ____
5. Have you experienced any positive effects after using stimulants?
☐ No
☐ Yes—please specify: ____
6. How did you obtain stimulants?
☐ I have a medical prescription
☐ I received them from friends
☐ I purchased them
7. Do you work during your studies?
☐ No
☐ Yes—average number of work hours per week: ____
8. Where do you live during the week?
☐ With parents
☐ In student dorms
☐ In a rented apartment
9. What are the reasons you use stimulants? If you do not use them, what do you think are the reasons other students do? (Check all that apply)
☐ Due to ADHD
☐ To improve concentration while studying
☐ To enhance academic performance
☐ To stay awake/study for longer hours
☐ To improve memorization of course material
☐ Because everyone else is using them
☐ To escape/disconnect from reality
☐ To lose weight/suppress appetite
☐ To improve athletic performance
☐ Other: ______
Attitudes Toward Stimulant Use
| Sometimes students without ADHD use stimulants for cognitive enhancement | 1 | 2 | 3 | 4 | 5 |
| Stimulants help improve academic performance in individuals without ADHD | 1 | 2 | 3 | 4 | 5 |
| It is acceptable to use stimulants to improve concentration while studying for exams | 1 | 2 | 3 | 4 | 5 |
| Stimulants may be harmful to health | 1 | 2 | 3 | 4 | 5 |
| It is fair for students to take stimulants to enhance their cognitive abilities | 1 | 2 | 3 | 4 | 5 |
| I would feel pressured to take stimulants if I knew other students were using them to enhance performance | 1 | 2 | 3 | 4 | 5 |
| Poor academic performance justifies the use of stimulants | 1 | 2 | 3 | 4 | 5 |
| Physicians should be allowed to prescribe stimulants for cognitive enhancement in individuals without ADHD | 1 | 2 | 3 | 4 | 5 |
Self-efficacy
| I can achieve most of the goals I set for myself | 1 | 2 | 3 | 4 | 5 |
| When facing difficult tasks, I am confident that I can accomplish them | 1 | 2 | 3 | 4 | 5 |
| In general, I believe I can achieve what matters to me | 1 | 2 | 3 | 4 | 5 |
| I can succeed in any task when I am determined to do so | 1 | 2 | 3 | 4 | 5 |
| I believe I can successfully overcome many challenges | 1 | 2 | 3 | 4 | 5 |
| I am confident in my ability to perform most tasks well | 1 | 2 | 3 | 4 | 5 |
| Compared to others, I can perform most tasks effectively | 1 | 2 | 3 | 4 | 5 |
Ajzen, I. The theory of planned behavior. Organizational Behavior and Human Decision Processes; 1991; 50,
Alavi, H. R. The role of self-esteem in tendency towards drugs, theft and prostitution. Addiction & Health; 2011; 3,
Allahverdipour, H.; Farhadinasab, A.; Galeeiha, A.; Mirzaee, E. Behavioral intention to avoid drug abuse works as protective factor among adolescent. Journal of Research in Health Sciences; 2007; 7,
Alon, S.; Gelbgiser, D. The female advantage in college academic achievements and horizontal sex segregation. Social Science Research; 2011; 40,
Arria, A. M.; Wilcox, H. C.; Caldeira, K. M.; Vincent, K. B.; Garnier-Dykstra, L. M.; O’Grady, K. E. Dispelling the myth of academic motivation as a primary factor in the nonmedical use of prescription stimulants among college students. BMC Public Health; 2018; 18,
Bandura, A. Self-efficacy: The exercise of control; W.H. Freeman: 1997.
Benson, K.; Alozai, F.; McKay, J. Cognitive and behavioral effects of prescription stimulant misuse among college students. Frontiers in Psychiatry; 2021; 12, 667118. [DOI: https://dx.doi.org/10.3389/fpsyt.2021.667118]
Bonny-Noach, H.; Ne’eman-Haviv, V. Medical and non-medical use of prescription stimulants: Association with cannabis, alcohol and tobacco use among university and college students in Israel. Israel Journal of Psychiatry and Related Sciences; 2018; 55,
Chemers, M. M.; Hu, L.; Garcia, B. F. Academic self-efficacy and first-year college student performance and adjustment. Journal of Educational Psychology; 2001; 93,
Chen, G.; Gully, S. M.; Eden, D. Validation of a new general self-efficacy scale. Organizational Research Methods; 2001; 4,
Compton, W. M.; Han, B.; Blanco, C.; Johnson, K.; Jones, C. M. Prevalence and correlates of prescription stimulant use, misuse, use disorders, and motivations for misuse among adults in the United States. The American Journal of Psychiatry; 2018; 175,
Dopelt, K.; Davidovitch, N.; Davidov, N.; Plot, I.; Boas, H.; Barach, P. “As if we are branded with the mark of Cain”: Stigma, guilt, and shame experienced by COVID-19 survivors in Israel—A qualitative study. Current Psychology; 2024; 43,
Erasmus, N.; Kotzé, C. Medical students’ attitudes towards pharmacological cognitive enhancement with methylphenidate. Academic Psychiatry; 2020; 44,
Figueroa, W.; Yomogida, K.; Mendez, J.; Bavarian, N. Prescription stimulant misuse avoidance self-efficacy: Correlates and moderation by race/ethnicity. Journal of Ethnicity in Substance Abuse; 2020; 19,
Ford, J. A.; Ong, J. Non-medical use of prescription stimulants for academic purposes among college students: A test of social learning theory. Drug and Alcohol Dependence; 2014; 144, pp. 279-282. [DOI: https://dx.doi.org/10.1016/j.drugalcdep.2014.09.011] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25270541]
Fuermaier, A. B. M.; Tucha, O.; Koerts, J.; Tucha, L.; Thome, J.; Faltraco, F. Feigning ADHD and stimulant misuse among Dutch university students. Journal of Neural Transmission; 2021; 128,
Giordano, A. L.; Prosek, E. A.; Reader, E. A.; Bevly, C. M.; Turner, K. D.; LeBlanc, Y. N.; Vera, R. A.; Molina, C. E.; Garber, S. A. Collegiate misuse of prescription stimulants: Examining differences in self-worth. Substance Use & Misuse; 2015; 50,
Glass, K.; Flory, K.; Martin, A.; Hankin, B. L. ADHD and comorbid conduct problems among adolescents: Associations with self-esteem and substance use. Attention Deficit and Hyperactivity Disorders; 2011; 3,
Hayes, A. F. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach; 2nd ed. Guilford Press: 2018.
Helmer, S. M.; Pischke, C. R.; Van Hal, G.; Vriesacker, B.; Dempsey, R. C.; Akvardar, Y.; Guillen-Grima, F.; Salonna, F.; Stock, C.; Zeeb, H. Personal and perceived peer use and attitudes towards the use of nonmedical prescription stimulants to improve academic performance among university students in seven European countries. Drug and Alcohol Dependence; 2016; 168, pp. 128-134. [DOI: https://dx.doi.org/10.1016/j.drugalcdep.2016.08.639]
Herman, L.; Shtayermman, O.; Aksnes, B.; Anzalone, M.; Cormerais, A.; Liodice, C. The use of prescription stimulants to enhance academic performance among college students in health care programs. The Journal of Physician Assistant Education; 2011; 22,
Hillman, N. Applying social learning theory constructs to better understand non-prescription stimulant use in college students. Master’s thesis; The University of Wisconsin-Whitewater: 2017; Available online: http://digital.library.wisc.edu/1793/75627 (accessed on 4 May 2025).
Hoshen, M. B.; Benis, A.; Keyes, K. M.; Zoëga, H. Stimulant use for ADHD and relative age in class among children in Israel. Pharmacoepidemiology and Drug Safety; 2016; 25,
Kadden, R. M.; Litt, M. D. The role of self-efficacy in the treatment of substance use disorders. Addictive Behaviors; 2011; 36,
Kennedy, S. Raising awareness about prescription and stimulant abuse in college students through on-campus community involvement projects. Journal of Undergraduate Neuroscience Education; 2018; 17,
Kolar, D.; Keller, A.; Golfinopoulos, M.; Cumyn, L.; Syer, C.; Hechtman, L. Treatment of adults with attention-deficit/hyperactivity disorder. Neuropsychiatric Disease and Treatment; 2008; 4,
Korn, L.; Hassan, K.; Fainshtein, N.; Yusov, N.; Davidovitch, N. Non-medical use of prescription stimulants for treatment of attention disorders by university students: Characteristics and associations. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research; 2019; 25, pp. 3778-3787. [DOI: https://dx.doi.org/10.12659/MSM.913973]
Lazarus, R. S.; Folkman, S. Stress, appraisal, and coping; Springer Publishing Company: 1984.
Looby, A.; Beyer, D. L.; Zimmerman, L. Non-medical prescription stimulant use: Investigating modifiable risk factors. Addiction Research & Theory; 2015; 23,
Maier, L. J.; Liechti, M. E.; Herzig, F.; Schaub, M. P. To dope or not to dope: Neuroenhancement with prescription drugs and drugs of abuse among Swiss university students. PLoS ONE; 2013; 8,
McCabe, S. E.; West, B. T.; Teter, C. J.; Boyd, C. J. Trends in medical and nonmedical use of prescription stimulants among US college students: Results from national surveys. Addiction; 2017; 112,
Merritt, A. C.; Effron, D. A.; Monin, B. Moral self-licensing: When being good frees us to be bad. Social and Personality Psychology Compass; 2010; 4,
O’Brien, M.; Crickard, E.; Lee, J.; Holmes, C. Attitudes and experience of youth and their parents with psychiatric medication and relationship to self-reported adherence. Community Mental Health Journal; 2013; 49,
Ponnet, K.; Tholen, R.; De Bruyn, S.; Wouters, E.; Van Ouytsel, J.; Walrave, M.; Van Hal, G. Students’ stimulant use for cognitive enhancement: A deliberate choice rather than an emotional response to a given situation. Drug and Alcohol Dependence; 2021; 218, 108410. [DOI: https://dx.doi.org/10.1016/j.drugalcdep.2020.108410]
Ponnet, K.; Wouters, E.; Walrave, M.; Heirman, W.; Van Hal, G. Predicting students’ intention to use stimulants for academic performance enhancement. Substance Use & Misuse; 2015; 50,
Reske, M.; Stewart, J. L.; Flagan, T. M.; Paulus, M. P. Attenuated neural processing of risk in young adults at risk for stimulant dependence. PLoS ONE; 2015; 10,
Rosenstock, I. M.; Strecher, V. J.; Becker, M. H. Social learning theory and the health belief model. Health Education Quarterly; 1988; 15,
Sabbe, M.; Sawchik, J.; Gräfe, M.; Wuillaume, F.; De Bruyn, S.; Van Antwerpen, P.; Van Hal, G.; Desseilles, M.; Hamdani, J.; Malonne, H. Use and misuse of prescription stimulants by university students: A cross-sectional survey in the French-speaking community of Belgium, 2018. Archives of Public Health; 2022; 80,
Sailo, H.; Varghese, D. Academic stress, its sources, effects and coping mechanisms among college students. International Journal of Science and Healthcare Research; 2024; 9,
Sattler, S.; Forlini, C.; Racine, E.; Sauer, C. Impact of contextual factors and substance characteristics on perspectives toward cognitive enhancement. PLoS ONE; 2013; 8,
Sattler, S.; Wiegel, C. Cognitive test anxiety and cognitive enhancement: The influence of students’ worries on their use of performance-enhancing drugs. Substance Use & Misuse; 2013; 48,
Sawchik, J.; Sabbe, M.; Gräfe, M.; Wuillaume, F.; Hamdani, J.; Malonne, H. Survey on prescription stimulant use among university students in Wallonia and Brussels: Prevalence, motives and perceived effects; Federal Agency for Medicines and Health Products: 2020; Available online: https://www.fagg-afmps.be/sites/default/files/content/methylphenidate_report.pdf (accessed on 12 December 2024).
Schunk, D. H.; Pajares, F. Wigfield, A.; Eccles, J. S. The development of academic self-efficacy. Development of achievement motivation; Academic Press: 2002; pp. 15-31.
Shavit, Y.; Ayalon, H.; Kurlaender, M. Schooling and inequality in Israeli society; Stanford University Press: 2008.
Shohani, M.; Chegeni, M.; Nasrollahi, A.; Gheshlaghi, L. A. Evaluating the knowledge, attitude, and performance of students from the Ilam university of medical sciences regarding the use of stimulants. Iranian Journal of Psychiatry and Behavioral Sciences; 2021; 15,
Sümbül-Şekerci, B.; Bildik, Ö.; Bektay, M. Y.; İzzettin, F. V. Attitudes of medicine, pharmacy, and dentistry students about psychostimulant use to enhance cognition. International Journal of Clinical Practice; 2021; 75,
Tam, C. C.; Benotsch, E. G.; Li, X. Self-esteem and non-medical use of prescription drugs among college students: Coping as a mediator. Substance Use & Misuse; 2020; 55,
Tate, S. R.; Wu, J.; McQuaid, J. R.; Cummins, K.; Shriver, C.; Krenek, M.; Brown, S. A. Comorbidity of substance dependence and depression: Role of life stress and self-efficacy in sustaining abstinence. Psychology of Addictive Behaviors; 2008; 22,
Teter, C. J.; McCabe, S. E.; LaGrange, K.; Cranford, J. A.; Boyd, C. J. Illicit use of specific prescription stimulants among college students: Prevalence, motives, and routes of administration. Pharmacotherapy; 2006; 26,
Tevet-Tubul, N. The contribution of continuing academic education to future orientation, self-efficacy, and optimism among individuals with cognitive disabilities: The case of “Kfar Tikva,” a supported housing framework for adults with special needs. Master’s thesis; University of Haifa, Faculty of Education: 2011; (In Hebrew)
Vargo, E. J.; Petróczi, A.; Shah, I.; Naughton, D. P. “It’s not cheating if everyone is doing it”: The role of social norms in attitudes toward cognitive enhancement. Substance Abuse Treatment, Prevention, and Policy; 2014; 9,
Verdi, G.; Weyandt, L. L.; Zavras, B. M. Non-medical prescription stimulant use in graduate students: Relationship with academic self-efficacy and psychological variables. Journal of Attention Disorders; 2016; 20,
Weinberger, A. H.; Platt, J.; Esan, H.; Galea, S.; Erlich, D.; Goodwin, R. D. Cigarette smoking is associated with increased risk of substance use disorder relapse: A nationally representative, prospective longitudinal investigation. The Journal of Clinical Psychiatry; 2017; 78,
Zajacova, A.; Lynch, S. M.; Espenshade, T. J. Self-efficacy, stress, and academic success in college. Research in Higher Education; 2005; 46, pp. 677-706. [DOI: https://dx.doi.org/10.1007/s11162-004-4139-z]
Zullig, K. J.; Divin, A. L.; Weiler, R. M.; Haddox, J. D.; Pealer, L. N. Adolescent nonmedical use of prescription pain relievers, stimulants, and depressants, and suicide risk. Substance Use & Misuse; 2015; 50,
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Abstract
Background: Non-medical use of prescription stimulants is increasing among college students worldwide. While intended for ADHD treatment, many students use these substances to improve their concentration and academic performance. Despite global research, little is known about the psychological and attitudinal factors influencing such use in the Israeli academic context. Objectives: We wished to examine the relationship between self-efficacy, attitudes toward stimulant use, and actual use among Israeli college students, aiming to uncover the mechanisms behind non-medical stimulant consumption. Methods: A cross-sectional online survey was conducted among 598 students from two Israeli academic institutions. The participants completed validated questionnaires assessing their demographic characteristics, stimulant use patterns, self-efficacy, and attitudes. Results: A total of 22% of students reported using stimulants, 17% of them without a prescription. Positive attitudes significantly increased the likelihood of use (Exp(B) = 3.31, p < 0.001), while higher self-efficacy reduced it (Exp(B) = 0.69, p < 0.01). A negative correlation was found between self-efficacy and favorable attitudes (r = −0.17, p < 0.001). The mediation analysis revealed that self-efficacy influences stimulant use entirely through its effect on attitudes toward stimulants. Additionally, stimulant use was significantly more common among Jewish students (25%) compared to non-Jewish students (11%; p < 0.05) and among smokers (36%) compared to non-smokers (20%; p < 0.001). Conclusions: Positive attitudes and low self-efficacy are key risk factors for stimulant misuse. These findings underscore the need for educational interventions aimed at strengthening self-efficacy and promoting healthier coping strategies in academic settings.
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Details
; Bord Shiran 2
; Houminer-Klepar Nourit 1 1 Department of Public Health, Ashkelon Academic College, Ashkelon 78211, Israel; [email protected]
2 Department of Health Systems Management, The Max Stern Yezreel Valley College, Emek Yezreel 19306, Israel; [email protected]