Psychological Predictors of Risky Sexual Behaviors in Nursing Students: A Multivariate Analysis

1. Introduction

Risk sexual behaviors (RSB) constitute a priority public health issue worldwide due to their association with sexually transmitted infections (STIs), unplanned pregnancies, and significant psychosocial consequences in young populations. According to recent estimates, more than one million STIs are acquired daily worldwide, disproportionately affecting adolescents and young adults (World Health Organization [WHO], 2024). It is estimated that between 30% and 60% of university students report at least one risk sexual behavior during their academic life, including inconsistent condom use and multiple sexual partners (Badillo-Viloria et al., 2020; Tapia-Martínez et al., 2020).

Understanding this phenomenon has been attributed to the interaction among biological, psychological, and social factors during the transition to emerging adulthood (Michelini et al., 2021). During this period, decision-making is influenced by greater independence, social pressure, and novelty seeking (Ortiz Tibanquiza & Vásquez de la Bandera, 2024). However, current evidence indicates that sexual health knowledge alone is insufficient to modify behaviors (Adaos-Soto et al., 2024). Consequently, contemporary explanatory models have incorporated psychological variables as key predictors of sexual behavior. Nevertheless, limitations persist in integrating these factors into multivariate predictive models (Elías-Risco & Chávez-Ventura, 2022).

In Latin America, this problem is intensified by inequalities in sexual education, access to health services, and social determinants that influence young people's sexual behavior. Regional reports indicate that between 35% and 55% of Latin American university students have engaged in risk sexual behaviors in the last year, particularly inconsistent contraceptive use and alcohol consumption prior to sexual relations (Pan American Health Organization [PAHO], 2023). In Mexico, recent studies show an increase in STIs among the population aged 18 to 24 years, representing one of the groups with the highest national incidence (Instituto Nacional de Salud Pública [INSP], 2023; Hubert et al., 2023).

Among health sciences students, particularly in Nursing, this issue acquires additional relevance due to the coexistence of clinical knowledge and personal risk behaviors. Recent research has demonstrated that even health sciences students maintain unsafe sexual practices, evidencing the disconnection between knowledge and behavior (Tapia-Martínez et al., 2020). Furthermore, academic workload and clinical stress may potentiate psychological factors associated with impulsive decisions (Maidana de Zarza et al., 2022). However, in Mexico there is still scarce empirical evidence integrating these factors into robust explanatory models.

The present study is grounded in the Psychosocial Vulnerability Model, which postulates that risk behaviors emerge from the interaction between dispositional factors (impulsivity, emotional intelligence) and situational psychological states (anxiety, academic stress). This model allows understanding how demands from the academic environment activate behavioral dysregulation mechanisms that increase vulnerability to unsafe sexual practices (Michelini et al., 2021; Betanzos Díaz & Paz Rodríguez, 2026).

Among the psychological factors associated with risk sexual behaviors, anxiety, academic stress, impulsivity, and emotional intelligence stand out. Anxiety has been related to alterations in cognitive processing, decreased self-control, and increased maladaptive coping behaviors, which raises the probability of risk sexual behaviors (Betanzos Díaz & Paz Rodríguez, 2026). Academic stress, highly prevalent among Nursing students, is associated with emotional exhaustion and coping strategies centered on immediate relief seeking, which may include impulsive sexual behaviors (Maidana de Zarza et al., 2022). On the other hand, impulsivity constitutes one of the most consistent predictors of risk behaviors in young people, reflecting the tendency to act without considering future consequences (Michelini et al., 2021).

In contrast, emotional intelligence has emerged as a relevant protective factor, favoring emotional regulation and adaptive decision-making (Ortiz Tibanquiza & Vásquez de la Bandera, 2024). Recent studies in nursing students have shown that higher levels of emotional intelligence are associated with lower participation in risk behaviors and better regulation of academic stress (Pérez-Fuentes et al., 2020). However, these factors have rarely been analyzed simultaneously in integrated multivariate models.

Despite growing scientific interest in risk sexual behaviors, current evidence presents important gaps: most studies are of simple cross-sectional correlational design, which limits the identification of the relative weight of each psychological variable (Tabares et al., 2020), and the literature has tended to analyze variables such as anxiety, academic stress, impulsivity, or emotional intelligence separately, without integrating their joint effect in a predictive model (Elías-Risco & Chávez-Ventura, 2022). This limitation is particularly relevant in Nursing students, a population scarcely studied in Latin America and Mexico, where the disconnection between clinical knowledge and risk behavior is evident (Tapia-Martínez et al., 2020). Consequently, regression models are required to explain the variance of RSB and allow the design of preventive interventions aimed not only at knowledge but also at emotional regulation and behavioral self-control (Adaos-Soto et al., 2024), a crucial aspect in Nursing students due to their future role as health promoters.

In this context, the present study aimed to determine the predictive value of emotional intelligence, anxiety, academic stress, and impulsivity on risk sexual behaviors in Nursing students. It was hypothesized that anxiety, academic stress, and impulsivity would be positively and significantly associated with risk sexual behaviors (H1a), while emotional intelligence would be negatively and significantly associated with such behaviors (H1b). Likewise, it was hypothesized that these psychological variables would contribute significantly to the prediction of risk sexual behaviors, even after controlling for the effect of sociodemographic variables such as age, sex, and academic semester (H2).

2. Materials and Methods

2.1. Study Design

A cross-sectional, analytical, and predictive study was conducted with a quantitative approach and correlational-predictive scope. The dependent variable was risk sexual behavior (RSB), while the predictor variables included emotional intelligence, anxiety, academic stress, and impulsivity. The model was adjusted for sociodemographic variables (age, sex, and academic semester) for statistical control purposes.

2.2. Population, Sampling, and Sample

The population consisted of 940 students enrolled in the Bachelor's Degree in Nursing at a Mexican public university during the 2025–2026 academic period. A non-probabilistic convenience sampling was employed, justified by the voluntary nature of the study and participant accessibility. A minimum sample size of 200 participants was estimated to detect a medium effect (f² = .15) with 80% power and α = .05, considering 7 predictors. Inclusion criteria were: (a) being a regular student in the Bachelor's Degree in Nursing, (b) being ≥18 years old, (c) having initiated sexual life, and (d) agreeing to participate through electronic informed consent. Questionnaires with >10% omissions in main variables or invariant response patterns (e.g., always marking the same option) were excluded. The final sample comprised 257 students after the exclusion of 16 cases (exclusion rate = 5.9%).

2.3. Instruments

An ad hoc questionnaire was used for sociodemographic and academic characterization (age, sex, academic semester, marital status, employment status, sexual history).

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Emotional intelligence. The Spanish version of the Trait Meta-Mood Scale-24 (TMMS-24; Fernández-Berrocal, Extremera & Ramos, 2004), adapted from the original instrument by Salovey et al. (1995), was administered. It consists of 24 items on a 5-point Likert scale (1 = totally disagree; 5 = totally agree), distributed across three dimensions: emotional attention, emotional clarity, and emotional repair. Higher scores reflect greater perceived emotional intelligence. Internal consistency in the present sample was α = .963; ω = .964.

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Anxiety. The Beck Anxiety Inventory (BAI; Beck, Epstein, Brown & Steer, 1988) was used, consisting of 21 items assessing anxious symptoms during the last week on a 4-point Likert scale (0 = not at all; 3 = severely). The total score ranges from 0 to 63; higher scores indicate greater anxiety. The obtained reliability was α = .946; ω = .948.

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Academic stress. The SISCO SV-21 Inventory (Barraza-Macías, 2018) was administered, consisting of 21 items distributed across three dimensions: academic stressors, symptoms, and coping strategies, with a 6-point Likert scale. Higher scores reflect greater academic stress. Internal consistency was α = .898; ω = .900.

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Impulsivity. The Barratt Impulsiveness Scale-11 (BIS-11; Patton, Stanford & Barratt, 1995) was employed, consisting of 30 items on a 4-point Likert scale, grouped into cognitive, motor, and non-planning impulsivity. Higher scores indicate greater impulsivity. Reliability was α = .900; ω = .901.

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Risk sexual behavior (RSB). The Risk Sexual Behavior Index (Ingledew & Ferguson, 2007) was applied, with 8 items assessing sexual activity, number of partners, unprotected relationships, alcohol/drug use before sex, STI testing, pregnancy testing, and frequency of protective method use. Items are coded so that higher scores reflect greater exposure to risk sexual behaviors. Internal consistency was α = .705.

2.4. Data Collection Procedure

Data were collected through an electronic questionnaire designed in Google Forms during March and April 2026. Prior institutional authorization was obtained from the Academic Area of Nursing of the participating university. The access link to the questionnaire was distributed through institutional email addresses and official academic platforms.

Upon accessing the form, participants viewed an information sheet with the objectives, procedures, benefits, and potential risks of the study. Subsequently, they provided their electronic informed consent; only those who agreed to participate had access to the complete questionnaire. The average response time ranged between 20 and 25 minutes; to mitigate fatigue, participants could pause the questionnaire and continue later. No economic or academic incentives were offered to avoid selection bias.

Data were anonymized from collection and stored in a protected database through digital security mechanisms and restricted access exclusively to the research team.

2.5. Statistical Analysis

Analyses were performed using IBM SPSS Statistics v.25 and Jamovi v.2.7. Categorical variables were described using frequencies and percentages, while continuous variables were summarized using means and standard deviations.

Internal consistency of the instruments was evaluated using Cronbach's alpha (α) and McDonald's omega (ω). Normality was examined using the Shapiro-Wilk test and skewness and kurtosis coefficients. Bivariate associations between variables were estimated using Pearson correlations.

To identify predictors of risk sexual behavior, a hierarchical multiple linear regression was performed in blocks. In the first block, sociodemographic variables (age, sex, and academic semester) were included as control variables. In the second block, emotional intelligence was incorporated, considered a dispositional factor. The third block integrated anxiety and academic stress, due to their nature as state variables, while in the fourth block, impulsivity was added as a dispositional factor associated with risk sexual behavior. The incremental contribution of each block to the model was evaluated through the change in the coefficient of determination (ΔR²). Finally, missing data, which accounted for less than 2% in all variables, were imputed using the mean substitution method of the corresponding scale.

Previously, the assumptions of linearity, independence of errors, normality of residuals, homoscedasticity, and absence of multicollinearity were verified using the Durbin-Watson statistic, tolerance, and variance inflation factor (VIF). Results were reported using unstandardized coefficients (B), standard errors (SE), standardized coefficients (β), 95% confidence intervals, and p-values. A two-tailed significance level of p < .05 was adopted.

2.6. Ethical Considerations

The research was conducted in accordance with the ethical principles established in the Declaration of Helsinki (2013) and with the provisions of the Regulation of the General Health Law on Research for Health in Mexico. The protocol was evaluated and approved by the Ethics and Research Committee of the participating institution (registration CEI-ICSa-2026/R017).

Participation was entirely voluntary. Anonymity and confidentiality of the information provided were guaranteed, as well as the participants' right to withdraw from the study at any time without academic or personal repercussions. Informed consent was obtained electronically before starting the questionnaire.

3. Results

3.1. Sample Description

The sample consisted of 257 university students, with a mean age of 21.37 years (SD = 2.18). A predominance of females was observed (75.5%), and the academic distribution was concentrated mainly in intermediate and advanced semesters, with a median of fourth semester (IQR: 2–5).

Regarding health status, the presence of chronic diseases was infrequent (1.6%), and most participants did not report being under psychological treatment at the time of evaluation (89.8%). Concerning lifestyle habits, regular physical activity practice showed a balanced distribution between those who practiced it (50.2%) and those who did not (49.8%). Furthermore, a considerable proportion of students perceived a high academic workload (74.7%), and most reported having had sexual relations (72.0%).

Table 1. Sociodemographic, Academic, and Health Characteristics of Participating Students (n = 257).

Variables	n (%) / M ± SD
Sex
Female	194 (75.5)
Male	63 (24.5)
Academic semester, Md (IQR)	4.00 (2–5)
First semester	5 (1.9)
Second semester	30 (11.7)
Third semester	49 (19.1)
Fourth semester	31 (12.1)
Fifth semester	66 (25.7)
Sixth semester	17 (6.6)
Seventh semester	59 (23.0)
Chronic disease
Yes	4 (1.6)
No	253 (98.4)
Psychological treatment
Yes	26 (10.2)
No	230 (89.8)
Regular physical activity
Yes	129 (50.2)
No	128 (49.8)
High academic workload
Yes	192 (74.7)
No	65 (25.3)
Sexual relations
Yes	185 (72.0)
No	72 (28.0)

Note. Data are presented as absolute frequency and percentage for categorical variables. For academic semester, the median (Md) and interquartile range (IQR) are reported. Percentages were calculated based on the total sample.

Table 1. Sociodemographic, Academic, and Health Characteristics of Participating Students (n = 257).

Variables	n (%) / M ± SD
Sex
Female	194 (75.5)
Male	63 (24.5)
Academic semester, Md (IQR)	4.00 (2–5)
First semester	5 (1.9)
Second semester	30 (11.7)
Third semester	49 (19.1)
Fourth semester	31 (12.1)
Fifth semester	66 (25.7)
Sixth semester	17 (6.6)
Seventh semester	59 (23.0)
Chronic disease
Yes	4 (1.6)
No	253 (98.4)
Psychological treatment
Yes	26 (10.2)
No	230 (89.8)
Regular physical activity
Yes	129 (50.2)
No	128 (49.8)
High academic workload
Yes	192 (74.7)
No	65 (25.3)
Sexual relations
Yes	185 (72.0)
No	72 (28.0)

Note. Data are presented as absolute frequency and percentage for categorical variables. For academic semester, the median (Md) and interquartile range (IQR) are reported. Percentages were calculated based on the total sample.

The mean scores for psychological variables and risk sexual behavior were: emotional intelligence (M = 50.58; SD = 20.58), anxiety (M = 18.54; SD = 13.85), stress (M = 41.21; SD = 12.67), impulsivity (M = 69.15; SD = 14.00), and risk sexual behavior (M = 7.19; SD = 3.66).

Females showed higher levels of anxiety (M = 19.35 vs. 16.12; t(255) = 2.01, p = .045), but no significant differences were observed in RSB by sex (t(255) = 1.68, p = .093). Skewness and kurtosis coefficients were within acceptable ranges for parametric tests (|skewness| < 2; |kurtosis| < 7) for all variables, although age showed a leptokurtic distribution with a ceiling effect.

Table 2. Descriptive Statistics of Continuous Study Variables (n = 257).

Variable	M	SD	Min.	Max.	Skewness	Kurtosis
1. Age	21.37	2.18	18.00	31.00	1.33	2.81
2. Emotional Intelligence	50.58	20.58	0.00	96.00	-0.12	-0.33
3. Anxiety	18.54	13.85	0.00	63.00	0.80	0.00
4. Stress	41.21	12.67	1.00	82.00	-0.34	1.28
5. Impulsivity	69.15	14.00	30.00	102.00	-0.36	0.69
6. RSB	7.19	3.66	2.00	19.00	0.82	0.50

Note. RSB = Risk Sexual Behavior; M = mean; SD = standard deviation; Min. = minimum value; Max. = maximum value. Skewness and kurtosis coefficients are presented as indicators of variable distribution. Absolute skewness values below 2 and kurtosis values below 7 suggest acceptable deviation from normality for parametric analyses.

Table 2. Descriptive Statistics of Continuous Study Variables (n = 257).

Variable	M	SD	Min.	Max.	Skewness	Kurtosis
1. Age	21.37	2.18	18.00	31.00	1.33	2.81
2. Emotional Intelligence	50.58	20.58	0.00	96.00	-0.12	-0.33
3. Anxiety	18.54	13.85	0.00	63.00	0.80	0.00
4. Stress	41.21	12.67	1.00	82.00	-0.34	1.28
5. Impulsivity	69.15	14.00	30.00	102.00	-0.36	0.69
6. RSB	7.19	3.66	2.00	19.00	0.82	0.50

Note. RSB = Risk Sexual Behavior; M = mean; SD = standard deviation; Min. = minimum value; Max. = maximum value. Skewness and kurtosis coefficients are presented as indicators of variable distribution. Absolute skewness values below 2 and kurtosis values below 7 suggest acceptable deviation from normality for parametric analyses.

Univariate normality of the items of each scale was assessed using the Shapiro-Wilk test. Results indicated significant deviations from normality in all items of the TMMS-24, the Beck Anxiety Inventory, the BIS-11, the SISCO SV-21 Scale, and the Risk Sexual Behavior Scale. Although significant deviations were found (p < .05), skewness and kurtosis coefficients were within acceptable ranges (|skewness| < 2; |kurtosis| < 7), justifying the use of parametric tests.

3.2. Correlations Between Variables

To test the proposed hypotheses, Pearson correlations were calculated (Table 3). As hypothesized, risk sexual behavior was positively and significantly associated with anxiety (r = .273, p < .001), stress (r = .345, p < .001), and impulsivity (r = .234, p < .001). Contrary to expectations, emotional intelligence did not correlate with risk sexual behavior (r = .028, p = .329), although it did show negative and significant associations with stress (r = -.326, p < .001) and impulsivity (r = -.389, p < .001).

Correlations between predictor variables and RSB ranged from low (r = .234, impulsivity) to moderate (r = .345, stress), indicating that stress is the psychological factor most strongly associated with RSB in the bivariate analysis. The absence of correlation between emotional intelligence and RSB (r = .028, p = .329) suggests that its effect could be indirect, mediated by other variables.

Correlations among psychological distress variables were all positive and significant, with the highest magnitude between anxiety and stress (r = .513, p < .001).

3.3. Hierarchical Linear Regression Analysis

A hierarchical multiple linear regression analysis was performed to identify predictors of risk sexual behavior. Prior assumptions of linearity, independence of errors (Durbin-Watson = 1.713), normality of residuals, homoscedasticity, and absence of multicollinearity (tolerance > .50; VIF < 2.0) were verified.

As shown in Table 4, Model 1, which included sociodemographic variables (age, sex, and semester), was statistically significant (F(3, 253) = 6.726, p < .001) and explained 7.4% of the variance in risk sexual behavior (R² = .074). The inclusion of emotional intelligence in Model 2 did not produce a significant increment in explained variance (ΔR² = .000, ΔF(1, 252) = 0.054, p = .816), indicating that this variable does not contribute to prediction beyond sociodemographic factors.

Model 3, which added anxiety and stress, produced a significant increment in explained variance (ΔR² = .112, ΔF(2, 250) = 17.268, p < .001), reaching an R² = .186. This suggests that psychological distress, particularly stress, constitutes a relevant factor in predicting risk sexual behavior. Finally, the incorporation of impulsivity in Model 4 did not significantly increase the explained variance (ΔR² = .006, ΔF(1, 249) = 1.975, p = .161); therefore, the final model was retained with the variables from Model 3 as the main predictors.

The global model was statistically significant (F(7, 249) = 8.499, p < .001) and explained 19.3% of the variance in risk sexual behavior (adjusted R² = .170).

The loss of significance of impulsivity when stress was included suggests a possible mediation effect; however, this interpretation requires confirmation through specific analyses (Sobel test = 2.84, p = .004).

3.4. Final Model Coefficients

In the final model (Table 5), age (β = .170, p = .008) and academic stress (β = .246, p = .002) emerged as the only statistically significant predictors of risk sexual behavior. Sex (β = .098, p = .093), academic semester (β = .050, p = .431), emotional intelligence (β = -.110, p = .088), anxiety (β = .113, p = .095), and impulsivity (β = .097, p = .161) did not reach statistical significance in the multivariate model. The effect size of the final model was f² = .239, considered moderate according to Cohen's criteria (1988).

4. Discussion

The present study aimed to determine the predictive value of emotional intelligence, anxiety, academic stress, and impulsivity on risk sexual behaviors in Nursing students. The findings partially confirmed the proposed hypotheses, evidencing that, in a multivariate model, academic stress and age emerged as the most significant predictors of RSB, explaining, together with the other sociodemographic variables, 19.3% of the total variance. This percentage of variance underscores the multifactorial nature of sexual behavior and highlights that psychological distress plays a preponderant role in exposure to risk practices in this specific population, suggesting that clinical knowledge acquired during training is insufficient to prevent risk behaviors (Tapia-Martínez et al., 2020). This reaffirms what has been alerted by regional reports on the persistence of unsafe practices among Latin American university students (PAHO, 2023).

Following the order of the predictive model, regarding sociodemographic variables, age proved to be a positive and significant predictor, indicating that older age is associated with greater exposure to risk sexual behaviors. These results are consistent with the findings of Michelini et al. (2021), who documented that chronological and academic advancement during the university stage is usually accompanied by an increase in experimentation and a higher frequency of sexual relations, which raises the statistical probability of incurring risks if consistent use of preventive methods does not intervene. Furthermore, the transition to emerging adulthood is characterized by greater independence, less supervision, and changing social pressure, factors that, added to the growing academic demands of advanced semesters, consolidate less restrictive behavioral patterns (Rizk et al., 2025).

Contrary to the initial hypothesis, emotional intelligence did not directly correlate with RSB nor did it contribute explained variance to the predictive regression model after controlling for sociodemographic variables. This result differs from that reported by Ortiz Tibanquiza and Vásquez de la Bandera (2024) and Pérez-Fuentes et al. (2020), who identified that adequate emotional regulation and repair act as a direct protective factor for the perception and prevention of sexual risks. However, in the present study, emotional intelligence did show negative and significant associations with stress and impulsivity. This suggests that, rather than having a direct effect on sexual behavior, emotional intelligence could exercise an indirect mediating role, attenuating the impact of academic stress, which is the true trigger of risk behavior. Previous evidence in health sciences supports that emotional regulation significantly decreases the symptoms of academic stressors (Pérez Briones et al., 2025), and promotes the deployment of adaptive coping strategies in the face of distress (Martínez et al., 2011). As indicated by Corral-Gil et al. (2023), the strengthening of socio-affective skills acts transversally, providing students with self-efficacy in situations of high vulnerability.

The finding that academic stress constitutes the main psychological predictor of RSB is consistent with previous evidence indicating how the severe demands of the university environment affect self-control. According to Maidana de Zarza et al. (2022), Andrade Ruiz and Garzón Acosta (2026), and Flores-Rojas et al. (2023), the high academic workload and clinical practices in Nursing predispose to emotional exhaustion, leading students to seek coping strategies centered on immediate relief. This premise is reinforced by recent research such as that of Cruz et al. (2023), who observed critical prevalence of stress in Mexican nursing students, directly impacting their adaptive responses and their emotional regulation dynamics in high-demand environments (Sierra Delgado et al., 2025). It is highly probable that stress acts as a mechanism that fatigues cognitive resources and decreases self-control, as warned by Betanzos Díaz and Paz Rodríguez (2026), reducing the capacity for deliberation in sexual decisions and fostering unprotected practices as emotional escape routes.

Anxiety showed a positive and significant bivariate association with RSB, reflecting its impact as a state of psychological distress that undermines self-regulation mechanisms. However, when integrating this variable into the multivariate model, its direct predictive capacity was attenuated, probably due to strong collinearity with academic stress. This finding is consistent with recent literature that places anxiety as an intrinsic component of the training environment in nursing, where the pressure to acquire clinical competencies and fear of error generate persistent states of emotional tension (Tirado-Reyes et al., 2024; De la O-Martínez et al., 2024). Evidence suggests that anxiety does not act in isolation but rather conforms a profile of 'psychological distress' that increases the propensity to immediate relief-seeking behaviors, including risky sexual practices as a maladaptive way to manage distress (Pittman et al., 2021). Thus, the anxious state seems to operate as a basal condition that, when potentiated by academic demands, weakens the critical judgment necessary for safe sexual decision-making, a phenomenon also observed in university populations where elevated levels of psychological distress correlate directly with inconsistency in the use of barrier methods (Pacompia & Rocha, 2022; Oyola-Villarroel & Alba-Javie, 2019).

Regarding impulsivity, although it showed a positive bivariate association with RSB, its predictive weight disappeared when incorporated in the last block of the multiple regression. This statistical phenomenon indicates that the variance that impulsivity shares with risk sexual behavior was almost entirely absorbed by stress and age. Although authors such as Tabares et al. (2020) and Michelini et al. (2021) described impulsivity as a key characteristic in disruptive behaviors among young people, in a high-demand environment such as nursing, it seems that the acute state of stress is what triggers impulsive behaviors, and not impulsivity itself. In other words, it is sufficient for a student to experience elevated levels of academic stress for their decision-making to be compromised, regardless of their baseline levels of impulsivity. This stress-mediated behavioral dysregulation is closely conceptually related to the loss of self-regulation observed in stressful academic tasks (Flores-Rojas et al., 2025).

Despite the relevance and methodological rigor of these findings, the present study has limitations that must be considered. First, the cross-sectional correlational design prevents establishing empirical causal relationships between academic stress, age, and risk sexual behaviors, so the directionality of the associations assumes a theoretical character. Second, data collection through self-report in self-administered questionnaires is susceptible to social desirability bias, a particularly sensitive factor when evaluating intimate behaviors in future health professionals, who know the expected normative standards. Third, the use of non-probabilistic sampling in a single institution limits the generalization of results to other university students enrolled in different curricula and sociocultural contexts. Finally, although the reliability of the instruments was adequate, the Risk Sexual Behavior Index showed a marginal Cronbach's alpha (α = .705), which could have affected the precision of the measurements of the dependent variable.

4.1. Limitations and Future Research

While this study provides valuable insights, several limitations should be acknowledged. The cross-sectional design precludes causal inferences, and longitudinal studies are needed to establish temporal relationships. The reliance on self-report measures may introduce social desirability bias, particularly given the sensitive nature of sexual behaviors. The non-probabilistic sample from a single Mexican public university limits generalizability to other populations and settings. Additionally, the marginal internal consistency of the Risk Sexual Behavior Index (α = .705) suggests that results concerning this variable should be interpreted with caution.

Future research should employ longitudinal designs to examine how risk behaviors fluctuate across academic periods and clinical training phases. Studies with probabilistic samples across multiple institutions would enhance generalizability. Furthermore, qualitative research could provide deeper understanding of the mechanisms linking academic stress to sexual risk behaviors. Intervention studies evaluating stress management and emotional regulation programs are needed to translate these findings into practice.

5. Conclusions

The predictive model analyzed evidences that academic stress and advancing age are the main psychosocial and demographic factors that increase vulnerability to unsafe sexual behaviors in Nursing students, above traits such as impulsivity. These results highlight an important paradox, given that health sciences students, although they possess advanced clinical training in sexually transmitted infections and pregnancy prevention, show a decrease in their self-care capacity when immersed in high academic demand environments. Likewise, it is concluded that emotional intelligence, although it does not directly protect against sexual risk, plays a vital role in the regulation of psychological distress that precedes such behaviors.

Based on these findings, the development of longitudinal designs is suggested to evaluate how risk behaviors fluctuate in concordance with critical periods of the semester, for example, final evaluations or the beginning of hospital practice. From the paradigm of Evidence-Based Nursing, it is imperative that higher education institutions integrate into their curricula interventions oriented to stress management and emotional regulation. These preventive strategies must go beyond the biomedical model of knowledge transmission, addressing sexual health as a phenomenon intrinsically linked to mental health, integral well-being, and adaptive coping strategies of the future health professional.

6. Patents