Post-traumatic Stress Disorder and Resilience in Medical Students: A Multicenter Study from 13 Latin American Countries

Abstract

Background

Despite efforts in the literature to address posttraumatic stress disorder (PTSD) linked to COVID-19, there is a knowledge gap in understanding the specific role of resilience as a potential mitigator in this scenario, particularly in the setting of future medical professionals.

Methods

We conducted a cross-sectional, multicenter study among medical students from 13 Latin American countries between June and September 2020, using a non-probabilistic convenience sampling strategy. PTSD symptoms, resilience, sleep quality, insomnia, burnout syndrome, physical activity, and socioeducational variables were assessed using validated instruments. Associations between PTSD and resilience, as well as other covariates, were evaluated using generalized linear models with Poisson distribution and robust variance, reporting prevalence ratios (PR) and 95% confidence intervals (95% CI). A total of 2019 medical students were included in the analysis.

Results

The prevalence of PTSD was 12.3% (95%CI: 10.88-13.79) and 37.5% had high resilience (95%CI: 35.38-39.65). Students with high levels of resilience had a 37% lower prevalence of PTSD compared with students with low resilience (PR: 0.63).

Conclusions

Higher levels of resilience were associated with a lower prevalence of PTSD symptoms among medical students. These findings underscore the substantial psychological impact of the COVID-19 pandemic on this population and support the implementation of institutional resilience-building and mental health support strategies, integrated into medical education, as a preventive approach to mitigate the mental health consequences of future epidemics and health-related disasters.

Keywords

posttraumatic stress mental health pandemic COVID-19 medical students Latin America

Introduction

In December 2019, Coronavirus Disease 2019 (COVID-19) was identified in Wuhan, China.¹ On January 30, 2020, the World Health Organization (WHO) declared the outbreak a global public health emergency of international concern.² This event led to the adoption of unprecedented measures, including the suspension of face-to-face activities; in the educational field, this forced a rapid transition to e-learning.³ Beyond its physical health consequences,^4–10 the pandemic had a substantial negative impact on mental health across different populations, including the general population,¹¹ military personnel,^12–14 healthcare workers,¹⁵ and medical students^16–18

Among the mental health outcomes associated with pandemic-related stressors, post-traumatic stress disorder (PTSD) has emerged as a major concern due to its close association with exposure to traumatic events. PTSD is defined as a psychological condition characterized by maladaptive responses—such as intrusive thoughts, avoidance, negative alterations in cognition and mood, and hyperarousal—that may develop following exposure to actual or threatened death, serious injury, or severe stressors.¹⁹ Conversely, resilience—understood as an adaptive coping process in the face of stressful events such as the COVID-19 pandemic—has been identified as a key protective factor that may reduce vulnerability to adverse mental health outcomes, including PTSD.^20,21

Medical students have consistently reported high levels of stress since the beginning of their training.^22,23 In relation to resilience, previous studies have shown that students with lower levels of resilience are up to three times more likely to present PTSD symptoms.²⁴ Conversely, those who develop higher resilience tend to report lower levels of psychological distress. However, existing studies examining PTSD and resilience among students present important methodological limitations that restrict the interpretation and generalizability of their findings. First, the frequent use of ecological or aggregate-level designs limits individual-level inference and precludes adequate adjustment for relevant psychosocial confounders, such as family dynamics and interpersonal stress.²⁵ Second, many studies rely on small or geographically restricted samples—often limited to a single country, city, or university—with low response rates and non-probabilistic sampling, which increases the risk of selection bias and limits external validity^{15,24,26–32} Third, the use of non-validated instruments and incomplete reporting of associations may introduce measurement bias and hinder comparability across studies.³³

Although mental health outcomes among healthcare workers in Latin America during the COVID-19 pandemic have been widely reported, particularly anxiety, depression, and burnout,³⁴ evidence focusing specifically on PTSD and resilience among medical students—especially from large, multicenter samples—remains limited. This gap is particularly relevant given that medical students represent a unique transitional population, exposed early to high academic pressure, clinical training environments, and health system stressors, often without the professional experience, autonomy, or institutional support available to fully trained healthcare workers. Understanding PTSD and resilience in this group is therefore essential to inform timely preventive strategies and mental health interventions during current and future public health crises.^35–37

In addition to resilience,³⁸ previous evidence has shown that factors such as sleep disturbances,³⁹ insomnia,⁴⁰ burnout,⁴¹ physical activity,⁴² and socioeducational characteristics^17,43 are closely related to mental health outcomes among medical students, particularly in high-stress contexts such as pandemics. These factors may act as contextual or confounding variables influencing the relationship between resilience and PTSD and therefore warrant consideration in multivariable analyses.

Therefore, the aim of this study was to identify the association between resilience and PTSD among medical students from 13 Latin American countries during the COVID-19 pandemic, as well as to explore other factors associated with the development of PTSD in this population.

Methods

Study Design

Multicenter analytic cross-sectional study whose purpose was to identify the association between resilience and PTSD in medical students from 13 Latin American countries during the period June 15 to September 15, 2020.

Population and Sample

This cross-sectional, multicenter study was conducted among medical students from 13 Latin American countries: Argentina, Bolivia, Brasil, Chile, Colombia, Cuba, Ecuador, El Salvador, Mexico, Paraguay, Panama, Peru and Venezuela

The population consisted of students enrolled in the 2020-I academic semester at medical schools in Latin American and Caribbean countries, and where there is a Scientific Society of Medical Students (SOCEM) or other scientific-academic group during the COVID-19 pandemic.

We included medical students who were enrolled in the regular 2020-I academic cycle at the universities where they were pursuing their undergraduate studies, who provided informed consent, and who were 18 years of age or older at the time of participation. The survey was disseminated using a non-probabilistic snowball sampling strategy, initially distributed through institutional mailing lists and academic channels of the participating universities and subsequently shared among medical students within their peer networks. Therefore, the total number of eligible students who received the invitation could not be precisely determined.

Eligible students voluntarily completed the online survey after providing informed consent. A total of 2019 students met the inclusion criteria and were included in the final analysis. We excluded students who did not respond to variables of interest in the questionnaires (n = 461) and those who did not provide informed consent (n = 200) (Figure 1).

Figure 1.

Participant selection flowchart.

The calculated sample size used the following information: a power of 80%, statistical significance at 95% and Pearson's correlation coefficient between post-traumatic stress symptoms and resilience traits of 0.108, considered in a previous study of American medical students without COVID-19.⁴⁴ A minimum sample of 670 students was obtained using the Epidat program. A 35% expected non-response rate and 40% rejection rate were added, resulting in a total number of 2680 eligible medical students. Finally, for the present analysis, 2019 students were taken as the final analytical sample. (Figure 1). This approach was used exclusively to ensure sufficient statistical power to detect an association between the variables of interest. In the analytical phase, associations were quantified using prevalence ratios estimated through generalized linear models with Poisson distribution and robust variance, which are appropriate for cross-sectional studies with binary outcomes.

Procedures

For enrollment, a local coordinator was designated at each participating university. In total, 64 universities from 13 Latin American countries were involved in the recruitment and data collection process. In Peru, coordinators were identified at each university, while in other Latin American countries recruitment was facilitated through the Federación Latinoamericana de Sociedades Científicas de Estudiantes de Medicina (FELSOCEM) network in other countries. Study information was disseminated via digital posters and student academic networks, and coordinators invited students to participate voluntarily after providing informed consent. Data collection was conducted during the early phase of the COVID-19 public health emergency, as declared by the governments of the participating countries, between June and September 2020.

Measurement was performed by means of surveys. Study data were collected and managed using Research Electronic Data Capture (REDCap), a secure, web-based platform that supports validated data capture and data management for research studies.^45,46 The survey was sent through the person in charge of each university, who in collaboration with the delegate of each year of study, was able to share the survey in the social network groups of each year. The time to fill out the questionnaire was approximately 20 min.

Instruments

All instruments used in this study have been previously validated and demonstrated adequate psychometric properties in Latin American and/or Spanish-speaking populations.

Post-traumatic stress disorder questionnaire (PCL-C): this questionnaire, consisting of 17 questions, follows the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV), as it explores the domains of trauma re-experiencing, trauma avoidance and blunting, and hyperarousal symptoms.⁴⁷ PTSD symptoms were assessed during the onset of social isolation measures against the COVID-19. It is based on a 1–5 point Likert scale, with total scores ranging from 17 to 85. The presence of PTSD symptoms is defined as a score of ≥43.^48–51 It presents excellent psychometric properties in evaluations carried out in Latin America,⁵² showing a sensitivity and specificity of 94% and 86%, respectively⁵³ together with a Cronbach's alpha of 0.94.⁴⁷

Resilience Questionnaire with Connor-Davidson Abbreviated Resilience Scale (CD-RISC): This questionnaire, made up of 10 questions, assesses resilience globally through a Likert scale with 5 options using a score from 0 to 4. It is interpreted as, the higher the score in each dimension, the more indicators^54,55 of resilience shown by the individual. It presents adequate internal validity (Cronbach's alpha=0.85) and construct validation⁵⁵; it shows excellent psychometric properties with a Cronbach's alpha = 0.86, r = 0.228–0.416, sensitivity = 70%, and specificity = 68.2% (49). Good validity and reliability in the Spanish-speaking population.^54,56–59 For this research, a cut-off point of ≥30 points was used for analytical purposes, consistent with previous research conducted in both COVID-19^60,61 and not COVID contexts.⁶²

Pittsburgh Sleep Quality Index Questionnaire (PSQI): A questionnaire that assesses sleep quality and its disturbances over the past 30 days.⁶³ It is made up of 7 components and subdivided into 19 self-assessment questions in order to obtain an overall score.⁶⁴ The components are: latency, subjective sleep quality, sleep disturbances, duration, use of sleep medication, daytime dysfunction, efficiency. The overall score ranges from 0 to 21 points, with a score of less than 5, “No sleep problems”, 5 to 7 “Deserves medical attention”, 8 to 14 “Deserves medical attention and treatment” and greater than 14, “It is a serious sleep problem”. This instrument presents adequate internal consistency (Cronbach's α = 0.83), a moderate correlation between its components (Pearson's r, 0.46 to 0.85).⁶³ Using the cut-off point of more than 5 points, consistent diagnostic accuracy (sensitivity: 89.6%; specificity: 86.5%) was observed in the identification of individuals with poor sleep quality.⁶⁴ It has been used in a Spanish version where they determined the quality of sleep and the degree of excessive daytime sleepiness in a Peruvian population of students during COVID context⁶⁵ and not COVID.⁶⁶ For analytical purposes, a global PSQI score >5 was used to define poor sleep quality.

Insomnia Questionnaire (ISI): This instrument measures in a self-reported way the perceived severity of your insomnia through 7 items. It has a Likert scale of 0–4 points and a final score between 0–28 points.⁶⁷ Higher scores reflect a higher degree of insomnia.^67,68 It also has excellent psychometric properties: Cronbach's alpha = 0.92, each individual item has an adequate discrimination capacity (r = 0.65-0.84), sensitivity 82.4%, specificity 82.1%.⁶⁹ This instrument has also been validated in a Spanish-speaking population,⁷⁰ and has shown through confirmatory factor analysis a three-factor structure that has a better fit, which are severity of sleeping difficulties at night, dissatisfaction with sleep and the impact of insomnia during the day.⁷¹

Burnout Syndrome with the Maslach Burnout Inventory: This instrument consists of 22 questions in the form of statements.⁷² It is considered the Gold Standard to identify the presence of Burnout Syndrome.⁷³ It is an instrument that evaluates the frequency of occurrence of the emotions and attitudes described in each of the statements in total, using a Likert-type scale with 7 levels (from “never = 0” to “every day = 6”). It has 3 dimensions: depersonalization, burnout and lack of personal fulfillment. It has been used and validated in several Latin American studies.^74,75 It has been validated in Peruvian medical students, finding an optimal indicator for factorial analysis according to the Kaiser-Meyer Olkin suitability test, which resulted in 0.813, Barlett showed significance (p < 0.000), supporting the relevance of the analysis. In addition, it has a Cronbach's Alpha coefficient of 0.794, highlighting notable correlations among the items, especially in Emotional Exhaustion (α=0.855), Cynicism (α=0.623) and Academic Efficacy (α=0.744).⁷⁶

Eating disorder questionnaire (EAT-26): This questionnaire consists of 26 questions using the Likert scale with six response types providing different scores (“never"=0, “rarely"=0, “sometimes"=0, “often"=1, “very often"=2, and “always"=3).⁶⁸ Only question 25 is scored in the opposite way.^77,78 Using 20 as the cutoff point for assuming an eating disorder, it has been validated in a female population (Cronbach's alpha = 0.92, cutoff value of >= 11; sensitivity of 100% and specificity of 85.6%)⁷⁸ and male in Colombia (Cronbach's alpha=0.89 and the best cut-off value of ≥20 points with a sensitivity=100% and specificity=97.8%).⁷⁹

Physical Activity Questionnaire (IPAQ-S): The short version of this instrument has 9 items and assesses self-reported physical activity in the past 7 days.⁸⁰ The data collected from the short version are the recorded physical activities (walking, moderate and intense), which will be converted to Metabolic Equivalent Tasks (METs).⁸⁰ Physical activity categories of walking (3.3 METs), moderate (4 METs) and intense (8 METs) will be obtained depending on the METs. Subsequently, a weighted MET-minutes per week of walking, moderate and intense activities will be estimated. These results will allow us to add them up and obtain a weighted estimate of total physical activity from all reported activities per week, with which we can categorize physical activity as: intense, moderate, mild or inactive.⁸⁰ The reliability of the short version showed correlation coefficients greater than 0.65 in 75% of them, as well as a moderate correlation against accelerometer measurements (r = 0.30; 95%CI: 0.23-0.36).^80,81 This instrument has Spanish versions that have been validated in Spanish-speaking populations and applied in Latin American populations.^79,82,83 The short version has shown significant correlations (0.26-0.69) in relation to accelerometer measurements in Spanish-speaking populations.⁸⁴

General, socio-educational and other data: General data included sociodemographic characteristics, health status, socio-educational factors, and COVID-19–related variables. These encompassed demographic information, academic characteristics, health conditions, and pandemic-related exposures and perceptions, as detailed in the Variables section.

Variables

The dependent variable was Post-Traumatic Stress Disorder (PTSD) due to COVID-19, operationally defined as a score of 43 or more points. This score was obtained by summing the scores of the 17 PTSD symptom questions of the PCL-C questionnaire. The main independent variable was resilience, operationally defined as a score greater than 30 points (high level of resilience) or less than 30 points (low level of resilience) of the abbreviated CD-RISC instrument.⁸⁵

Additionally, secondary independent variables were operationalized using established cut-off points from validated instruments. Insomnia was defined as an Insomnia Severity Index (ISI) score of ≥8 and categorized as absence of clinical insomnia (0-7), subclinical insomnia (8-14), moderate insomnia (15-21), and severe insomnia (22-28), in line with standard recommendations.⁸⁶ Sleep quality was classified as poor when the Pittsburgh Sleep Quality Index (PSQI) global score exceeded 5. Burnout syndrome was defined by high levels of emotional exhaustion and depersonalization together with low personal accomplishment, using the 66th and 33rd percentiles as cut-off points, respectively. Risk of eating disorder was defined as an EAT-26 score >20. Physical activity was categorized as low, moderate, or high based on total metabolic equivalent minutes per week, according to the International Physical Activity Questionnaire–Short Form (IPAQ-S) scoring protocol.⁸⁰

General variables were collected, including age in years (continuous); sex (female, male; dichotomous); country of origin (Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, Ecuador, El Salvador, Mexico, Paraguay, Panama, Peru, Venezuela; polytomous); marital status (single, married, cohabiting, widowed, divorced; polytomous), which was subsequently dichotomized as single versus not single; religion (none, Catholic, non-Catholic; polytomous); having children (no, yes; dichotomous); number of family members (continuous); and role in the family (child, parent, grandparent; polytomous), which was further dichotomized as having a child role versus not. COVID-19–related family exposure included having a family member diagnosed with COVID-19 (no, yes; dichotomous) and having a family member deceased due to COVID-19 (no, yes; dichotomous).

Health-related variables included body mass index (BMI), categorized as underweight (<18.5 kg/m²), normal weight (18.5-24.9 kg/m²), overweight (25.0-29.9 kg/m²), and obesity type I (30.0-34.9 kg/m²), type II (35.0-39.9 kg/m²), and type III (≥40.0 kg/m²) (polytomous), based on self-reported weight and height. Additionally, self-reported arterial hypertension (no, yes; dichotomous), diabetes mellitus (no, yes; dichotomous), personal history of mental health disorders (no, yes; dichotomous), and personal history of COVID-19 (no, yes; dichotomous) were recorded.

Educational variables included type of university (public, private; dichotomous), year of study (first to seventh year; polytomous), level of English language proficiency (none, basic, intermediate, advanced; polytomous), remote/virtual academic load (no, yes; dichotomous), affiliation with extracurricular groups (none, SOCEM, academic group, research group, other; polytomous), and participation in face-to-face and/or virtual training on COVID-19 (no, yes; dichotomous).

Psychosocial variables included compliance with social isolation measures (no, yes; dichotomous), perception of pandemic severity (very serious, serious, mild/not serious; polytomous), and confidence in the government's ability to manage the COVID-19 pandemic (no, yes; dichotomous).

Data Analysis

In the descriptive analysis, absolute and relative frequencies of categorical variables were shown. In the case of numerical variables, mean and standard deviation were reported for variables with normal distribution; otherwise, median and interquartile range were presented for those with non-normal distribution.

In the bivariate analysis, associations between resilience, PTSD, and other categorical variables were assessed using the chi-square test of independence, following verification of test assumptions, including adequate expected frequencies in contingency tables. In the case of numerical variables, the Mann–Whitney U test was used after assessing normality and observing a non-normal distribution. In the crude (unadjusted) model, the association of interest (resilience vs PTSD) was evaluated, together with the rest of the covariates. Generalized linear models, Poisson family, log link function and robust variance were used, using the host country as a cluster. The host country was specified as a clustering variable to account for within-country correlation among participants arising from the multicenter study design. Subsequently, we assessed whether adjustment for potential confounding variables changed the magnitude of the association between resilience and PTSD, comparing crude and adjusted prevalence ratios. Multivariable models were adjusted for sociodemographic (age, sex, marital status, religion, family characteristics), socio-educational (type of university, academic load, extracurricular activities, COVID-19 training), lifestyle and physical health (BMI category, hypertension, diabetes, physical activity), sleep-related and behavioral (insomnia severity, risk of eating disorder), mental health–related (burnout syndrome, personal mental health history), and COVID-19–related variables (history of COVID-19, family infection or death, perceived severity of the pandemic, compliance with social isolation measures, and confidence in governmental response).

Prevalence ratios (PR) and 95% confidence intervals (95%CI) were estimated. Multicollinearity among independent covariables included in the adjusted models was assessed using variance inflation factors (VIF), with no evidence of problematic collinearity detected. Statistical analysis was performed in Stata v.17.0 (StataCorp LP, College Station, TX, USA).

Ethical Aspects

The study was approved by the Research Ethics Committee for COVID-19 of the Seguro Social de Salud-EsSalud Lima. The ethical principles according to the Declaration of Helsinki were taken into account, the students were asked to accept the informed consent before accessing the questionnaire virtually. The principal investigator of the study was the only person who had access to the personal information shared, and anonymized databases were managed using numerical codes.

Results

Characteristics of the Study Sample

We analyzed a sample of 2019 medical students from 13 Latin American countries. We found that 64.6% were male (n = 1304), the median age was 21 years, 50.5% (n = 1019) were from Peru, 52% (n = 1040) were studying at a national university and 19.5% (n = 394) were in their second year of their degree. A total of 78.9% (n = 1593) reported that they were studying virtually, 20.8% were affiliated with a SOCEM and 57.8% (n = 1167) reported having received training on COVID-19 in person and/or virtually. In addition, 94.8% (n = 1913) reported being in compliance with social isolation measures and 62% (n = 1251) perceived the pandemic to be a very serious issue. 3.5% (n = 71) reported having had COVID-19 disease and 10.2% (n = 206) mentioned that a family member had died from this disease. 11.9% (n = 240) suffered from moderate clinical insomnia, 36.6% (n = 738) presented low level of physical activity. 11.2% (n = 227) mentioned having a previous history of mental illness and 1.4% (n = 29) presented Burnout syndrome. The prevalence of high resilience was 37.5% (95%CI: 35.38-39.65). The prevalence of post-traumatic stress disorder due to the COVID-19 pandemic was 12.3% (95%CI: 10.88-13.79) (Table 1).

Table 1.

Characteristics of Medical Students (n = 2019).

Characteristics	N (%)
Age (years)*	21 (19-23)
Sex
Female	715 (35.4)
Male	1304 (64.6)
Country
Argentina	71 (3.5)
Bolivia	152 (7.5)
Brazil	19 (0.9)
Chile	1 (0.1)
Colombia	434 (21.5)
Cuba	2 (0.1)
Ecuador	107 (5.3)
El Salvador	1 (0.1)
Mexico	4 (0.2)
Paraguay	58 (2.9)
Panama	1 (0.1)
Peru	1019 (50.5)
Venezuela	150 (7.4)
Type of university
National or Public	1049 (52.0)
Private	970 (48.0)
Study year**
First	389 (19.3)
Second	394 (19.5)
Third	364 (18.0)
Fourth	352 (17.4)
Fifth	292 (14.5)
Sixth	128 (6.3)
Seventh	99 (4.9)
Level of English language
None	136 (6.7)
Basic	895 (44.3)
Intermediate	684 (33.9)
Advanced	304 (15.1)
Remote/virtual academic load**
No	425 (21.1)
Yes	1593 (78.9)
Belongs to extracurricular group**
No	1314 (65.1)
SOCEM	419 (20.8)
Academic group	108 (5.4)
Research group	81 (4.0)
Another	96 (4.8)
In-person and/or virtual training on COVID-19
No	852 (42.2)
Yes	1167 (57.8)
Civil status
Single	1960 (97.1)
Married	21 (1.0)
Cohabitant	31 (1.5)
Widower	3 (0.2)
Divorced	4 (0.2)
Religion
None	491 (24.3)
Catholic	1305 (64.6)
Not Catholic	223 (11.1)
Having children
No	1954 (96.8)
Yes	65 (3.2)
Number of members in the family*	4.3 ± 1.71
Role in the family
Children	1965 (97.3)
Father/Mother	49 (2.4)
Grandfather/mother	5 (0.3)
BMI (categorized)
Underweight	82 (4.1)
Normal	1337 (66.2)
Overweight	502 (24.9)
Type 1 obesity	81 (4.0)
Type 2 obesity	11 (0.5)
Type 3 obesity	6 (0.3)
Arterial hypertension
No	1983 (98.2)
Yes	36 (1.8)
Diabetes mellitus
No	1995 (98.8)
Yes	24 (1.2)
Complies with social isolation measures
No	106 (5.3)
Yes	1913 (94.8)
Perception severity of the pandemic
Very serious	1251 (62.0)
Serious	735 (36.4)
Mild	27 (1.3)
Not serious	6 (0.3)
Confidence in the government's ability to manage the COVID-19 pandemic
No	1558 (77.2)
Yes	461 (22.8)
Personal history of COVID-19 infection
No	1948 (96.5)
Yes	71 (3.5)
Family member diagnosed with COVID-19
No	958 (47.5)
Yes	1061 (52.6)
Family member deceased from COVID-19
No	1813 (89.8)
Yes	206 (10.2)
Personal mental health history
No	1792 (88.8)
Yes	227 (11.2)
Sleep quality
Good	764 (37.8)
Bad	1255 (62.2)
Insomnia
Absence of clinical insomnia	1073 (53.2)
Subclinical insomnia	634 (31.4)
Moderate clinical insomnia	240 (11.9)
Severe clinical insomnia	72 (3.6)
Physical activity level
Low	738 (36.6)
Moderate	180 (8.9)
High	1101 (54.5)
Risk of eating disorder
Not clinically relevant	1814 (90.0)
Relevant	205 (10.2)
Burnout Syndrome
No	1990 (98.6)
Yes	29 (1.4)
Resilience
Low	1262 (62.5)
High	757 (37.5)
Post-traumatic stress disorder
No	1771 (87.7)
Yes	248 (12.3)

*Median (25th percentile - 75th percentile), SOCEM: Sociedad Cientifica de Estudiantes de Medicina

**Totals may not add up to 100% due to missing data.

Bivariate Analysis Between post-Traumatic Stress and Resilience, and Other Associated Factors

Students with high level of resilience had lower frequency of post-traumatic stress symptoms (7% vs 15.5%; p < 0.001). Additionally, we found statistically significant differences between PTSD and age (p = 0.008), gender (p = 0.002), type of university (p = 0.011), remote academic load (p = 0.003), body mass index (p = 0.016), reporting diabetes mellitus as background (p = 0. 011), reporting confidence in government ability to manage the pandemic (p = 0.007), reporting having sick (p < 0.001) and deceased (p = 0.001) family member (p = 0.001) by COVID-19, previous history of mental health (p < 0.001), sleep quality (p < 0.001), insomnia (p < 0.001), risk of eating disorder (p < 0.001) and Burnout syndrome (p < 0.001) (Table 2).

Table 2.

Characteristics Associated with Posttraumatic Stress Disorder, Bivariate Analysis.

Variables	Post-Traumatic Stress Disorder		p*
	No (n = 1771)	Yes (n = 248)
	n (%)	n (%)
Age (years)***	21 (19–23)	21 (19–22)	0.008**
Sex			0.002
Female	649 (36.7)	66 (26.6)
Male	1122 (63.4)	182 (73.4)
Type of university			0.011
National or Public	939 (53.0)	110 (44.4)
Private	832 (47.0)	138 (55.7)
Study year****			0.254
First	329 (18.6)	60 (24.3)
Second	346 (19.5)	48 (19.4)
Third	315 (17.8)	49 (19.8)
Fourth	315 (17.8)	37 (15.0)
Fifth	262 (14.8)	30 (12.1)
Sixth	117 (6.6)	11 (4.5)
Seventh	87 (4.9)	12 (4.9)
Remote/virtual academic load****			0.003
No	391 (22.1)	34 (13.8)
Yes	1380 (77.9)	213 (86.2)
Belongs to extracurricular group****			0.552
No	1149 (64.9)	165 (66.9)
Yes	622 (35.1)	82 (33.2)
In-person and/or virtual training on COVID-19			0.156
No	737 (41.6)	115 (46.4)
Yes	1034 (58.4)	133 (53.6)
Single marital status			0.762
No	51 (2.9)	8 (3.2)
Yes	1720 (97.1)	240 (96.8)
Religion			0.472
None	423 (23.9)	68 (27.4)
Catholic	1152 (65.1)	153 (61.7)
Not Catholic	196 (11.1)	27 (10.9)
Having children			0.995
No	1714 (96.8)	240 (96.8)
Yes	57 (3.2)	8 (3.2)
Number of members in the family***	4.32 ± 1.72	4.21 ± 1.68	0.355***
Role of children in the family			0.320
No	45 (2.5)	9 (3.6)
Yes	1726 (97.5)	239 (96.4)
BMI (categorized)			0.016
Under weight	71 (4.0)	11 (4.4)
Normal	1167 (65.9)	170 (68.6)
Overweight	455 (25.7)	47 (19.0)
Obesity	78 (4.4)	20 (8.1)
Arterial hypertension			0.067
No	1743 (98.4)	240 (96.8)
Yes	28 (1.6)	8 (3.2)
Diabetes Mellitus			0.011
No	1754 (99.0)	241 (97.2)
Yes	17 (1.0)	7 (2.8)
Complies with social isolation measures			0.365
No	90 (5.1)	16 (6.5)
Yes	1681 (94.9)	232 (93.6)
Perception severity of the pandemic			0.057
Very serious	1081 (61.0)	170 (68.6)
Serious	659 (37.2)	76 (30.7)
Mild/Not serious	31 (1.8)	2 (0.9)
Confidence in the government's ability to manage the COVID-19 pandemic			0.007
No	1350 (76.2)	208 (83.9)
Yes	421 (23.8)	40 (16.1)
Personal history of COVID-19 infection			0.227
No	1712 (96.7)	236 (95.2)
Yes	59 (3.3)	12 (4.8)
Family member diagnosed with COVID-19
No	871 (49.2)	87 (35.1)	<0.001
Yes	900 (50.8)	161 (64.9)
Family member deceased from COVID-19			0.001
No	1605 (90.6)	208 (83.9)
Yes	166 (9.4)	40 (16.1)
Personal mental health history			<0.001
No	1614 (91.1)	178 (71.8)
Yes	157 (8.9)	70 (28.2)
Sleep quality			<0.001
Good	732 (41.3)	32 (12.9)
Bad	1039 (58.7)	216 (87.1)
Insomnia			<0.001
Absence of clinical insomnia	1038 (58.6)	35 (14.1)
Subclinical insomnia	550 (31.1)	84 (33.9)
Moderate clinical insomnia	158 (8.9)	82 (33.1)
Severe clinical insomnia	25 (1.4)	47 (19.0)
Physical activity level			0.050
Low	630 (35.6)	108 (43.6)
Moderate	161 (9.1)	19 (7.7)
High	980 (55.3)	121 (48.8)
Risk of eating disorder			<0.001
Not clinically relevant	1636 (92.4)	178 (71.8)
Relevant	135 (7.6)	70 (28.2)
Burnout Syndrome			<0.001
No	1757 (99.2)	233 (94.0)
Yes	14 (0.8)	15 (6.1)
Resilience			<0.001
Low	1067 (60.3)	195 (78.6)
High	704 (39.8)	53 (21.4)

* p-value of categorical variables calculated with the Chi-Square test.

** p-value of categorical-numerical variables calculated with the U-test (Mann-Whitney).

*** Median - interquartile range.

****Totals may not add up to 100% due to missing data.

Association Between Posttraumatic Stress and Resilience in Simple and Multiple Regression Analysis

In the simple regression model, we found that the prevalence of PTSD decreased 55% in students with a high level of resilience (PR: 0.45; 95%CI: 0.37-0.56). In the multiple model, the association was maintained in magnitude and direction. Students with high resilience level showed 37% lower prevalence of PTSD; with respect to students with low resilience (PR: 0.63; 95%CI: 0.45-0.89) (Table 3 and Figure 2).

Figure 2.

Resilience and post-traumatic stress disorder in medical students.

Table 3.

Resilience and post-Traumatic Stress Disorder in Medical Students, in Simple and Multiple Regression Analysis.

Variables	Post-Traumatic Stress Disorder
	Simple regression			Multiple regression
	PR	IC 95%	p*	PR	IC 95%	p*
Resilience
Low	Ref.			Ref.
High	0.45	0.37–0.56	<0.001	0.63	0.45–0.89	0.009

*p-values obtained with Generalized Linear Models (GLM), Poisson family, log-link function, robust variance

Discussion

Prevalence of PTSD

We found that 12.3% of medical students presented PTSD during the COVID-19 pandemic. This prevalence is comparable to that reported in previous studies focusing on medical students or closely related trainee populations in Latin America. For instance, in Panama, 12.2% of medical interns and residents exhibited PTSD symptoms.²⁹ In Ecuador, 13.0% of medical students and faculty members reported PTSD symptoms.³¹ Additionally, studies conducted among medical students have reported PTSD prevalences ranging from 8.6% to 19.5%,^15,32 supporting the consistency of our findings within this population.

In contrast, some studies have reported higher PTSD prevalences in populations other than medical students. For example, a multicountry study conducted in Argentina, Colombia, Mexico, and Chile reported PTSD prevalences ranging from 19.9% to 29.8%, primarily among healthcare workers.⁸⁷ Similarly, a study conducted in Asia by Chi et al reported PTSD prevalences exceeding 30% during the first and second waves of the pandemic, primarily among college students and general populations.⁸⁸ Research conducted in China among international students identified that 37.5% experienced moderate to severe PTSD symptoms.⁸⁹ These higher prevalences may be partly explained by differences in population characteristics, including older age groups, occupational exposure, broader population samples, and the use of assessment instruments different from the PCL-C.

In the absence of large multicenter studies exclusively focused on medical students across Latin America, comparisons with other health-related populations provide contextual insight. Studies conducted in Peru reported a PTSD prevalence of 14.9% among the general population and healthcare workers,⁹⁰ while a prevalence of 7.2% was observed among healthcare workers in Paraguay.³⁰ Additional studies from Paraguay and Mexico,^91,92 although not exclusively focused on medical students, highlight the substantial psychological burden experienced by populations exposed to pandemic-related stressors. Nevertheless, differences in study populations, methodologies, and cultural contexts should be considered when interpreting these comparisons. These higher prevalences may be related to broader contextual factors shared by several Latin American countries, including prolonged social restrictions, economic instability, health system strain, and limited access to mental health services during the pandemic, which may have amplified psychological distress.⁹³

At a broader level, a meta-analysis by Yunitri et al⁹⁴ reported that the American continent had the lowest pooled prevalence of PTSD (8.08%) compared with Europe and Asia. However, this meta-analysis included heterogeneous populations—such as COVID-19 patients and survivors, healthcare professionals, and the general population—which limits direct comparability with studies conducted specifically among medical students.

Prevalence of Resilience

Almost four out of ten medical students presented high levels of resilience. This proportion is consistent with previous studies in similar populations. For example, Duarte et al⁹⁵ reported that approximately two and five out of ten students exhibited low and moderate resilience, respectively, while Sumen and Adibelli⁹⁶ found that around four out of ten students showed high resilience levels. The fact that fewer than half of the students demonstrated high resilience may be related to the broader pandemic context, during which increased levels of academic stress and burnout have been widely reported among medical students.⁹⁵ Additional factors that may contribute to lower resilience include prolonged social isolation, uncertainty regarding academic progression, disruption of clinical training, limited access to institutional mental health support, and reduced opportunities for peer interaction and coping skill development during remote education.⁹⁷

Different results have been reported in studies conducted before the COVID-19 pandemic. For example, a study carried out in a Peruvian medical school found that approximately seven out of ten students exhibited high levels of resilience.⁹⁸ Similarly, other studies conducted in Latin America reported high resilience levels among medical students, with proportions ranging from 70% to 90%^99–101 These higher resilience levels may be explained by contextual differences, including data collection outside pandemic-related stressors, greater academic stability, stronger social support networks, in-person educational environments, and the use of different resilience measurement instruments, which limits direct comparability with findings obtained during the COVID-19 pandemic.

Likewise, previous research has reported that medical students exhibited lower resilience levels compared with the general population, particularly among those in clinical years.³³ This finding may be explained not only by limited formal training in resilience, but also by increased exposure to real patients, greater academic demands, higher workload, and immersion in more stressful clinical environments during rotations.^102–104 Similarly, evidence indicates found that medical students had lower resilience compared with peers from other academic disciplines of the same age, suggesting reduced preparedness in coping skills. Importantly, coping behaviors and personal resilience were significantly associated with lower stress levels during confinement periods.¹⁰⁵ In this context, factors such as positive attitude, perception of self-efficacy,¹⁰⁶ and academic self-efficacy¹⁰⁷ have been identified as relevant protective elements that may enhance resilience among medical students.

Association Between post-Traumatic Stress and Resilience

Students with levels of resilience were associated with a lower prevalence of PTSD. This is consistent with previous findings indicating that higher levels of resilience are associated with a lower prevalence of PTSD among college students.⁸⁸ Likewise, studies conducted in Chile have shown that the prevalence of PTSD among medical students decreases with greater use of coping strategies and higher levels of resilience, with similar results observed in the adult population.^108,109 In addition, other research has reported that students who perceived the pandemic as more stressful presented greater posttraumatic growth.⁴⁴ Similarly, evidence indicates that medical students with lower levels of resilience were 3.32 times more likely to present symptoms of distress or peritraumatic stress.²⁴In this sense, higher levels of resilience have been associated with lower psychological distress, fewer anxiety symptoms, and greater satisfaction with daily life compared with the general population.^28,110 Similar findings have been reported among nursing students, in whom lower PCL-C scores were observed in those with higher resilience levels.¹¹¹ Moreover, a study conducted during the COVID-19 pandemic reported that 13.5% of individuals with PTSD exhibited low levels of resilience, further supporting the inverse association between these constructs.¹¹²

So far, it has been recognized that various stressful life events affect the normal function and structure of the brain, conditioning the development of PTSD,^113,114 therefore, these people have lower levels of psychological resilience,⁵⁴ however, this relationship also suggests a bidirectional association between the variables. One of the possible biological mechanisms underlying the observed association involves modulation of the hypothalamic–pituitary–adrenal (HPA) axis, which plays a central role in the physiological stress response.^113,114 Resilience, understood as the capacity to adapt and recover from adverse experiences, has been associated with more effective regulation of the HPA axis and lower cortisol secretion in response to stress.^115,116 Adequate regulation of cortisol may, in turn, facilitate neuronal plasticity and neurogenesis, particularly in brain regions implicated in stress and emotion regulation, such as the hippocampus and amygdala.¹¹⁷ These processes are essential for adaptive learning, emotional regulation, and recovery from traumatic stress, and may partly explain why individuals with higher resilience exhibit a lower prevalence of PTSD symptoms.

Other Factors Associated with PTSD

Each additional year of age was associated with a 4% lower prevalence of PTSD. This finding is consistent with studies reporting greater vulnerability to PTSD symptoms among younger individuals, whereas older adults tend to have fewer worries, greater social support, and more developed coping strategies, which have been associated with lower PTSD severity both in overall PTSD severity scores and across specific symptom domains^118–121 Evidence from large-scale meta-analyses conducted during the COVID-19 pandemic has shown lower pooled prevalence estimates of PTSD in older populations.⁹⁴ In contrast, other studies have reported higher PTSD severity among older individuals, particularly in specific pandemic contexts and populations.^88,122 Taken together, these mixed findings suggest that the association between age and PTSD may vary according to contextual, demographic, and psychosocial factors rather than reflecting a uniform pattern across settings.^123,124

Having a prior history of mental health problems was associated with a 61% higher prevalence of PTSD. This finding aligns with previous evidence showing that individuals with pre-existing mental health conditions are more vulnerable to posttraumatic stress symptoms when exposed to acute or unprecedented stressors, across both general and healthcare-related populations.^{29,91,123,125} Importantly, similar associations have also been reported among medical students, underscoring the relevance of this vulnerability within academic medical settings.¹²⁶ One possible explanation involves mental health–related stigma within medical communities, which may discourage help-seeking behaviors and contribute to underdiagnosis and delayed treatment, thereby increasing susceptibility to persistent PTSD symptoms. Supporting this interpretation, both psychological factors—such as peritraumatic distress and dissociation—and biological markers, including heightened noradrenergic activity, have been identified as predictors of PTSD onset and persistence.^127,128

Students engaged in remote academic activities were associated with a 24% higher prevalence of PTSD. This finding aligns with previous evidence indicating that prolonged remote learning and social isolation are associated with adverse mental health outcomes, including increased stress and posttraumatic symptoms among students.^129,130 Feelings of loneliness and reduced social interaction during remote education may partly explain this association. Differences with findings from studies conducted in healthcare worker populations—where close-contact activities were linked to poorer mental health outcomes—may reflect distinct exposure patterns and stressors inherent to professional clinical settings compared to academic environments.^34,131

Having a family member diagnosed with COVID-19 was associated with a 42% higher prevalence of PTSD. This finding is consistent with previous studies showing that indirect exposure to illness within close social networks increases psychological distress and posttraumatic symptoms.^27,31,132 Evidence from different populations indicates that having an infected or severely ill family member substantially elevates PTSD risk, likely due to heightened fear, uncertainty, and perceived lack of control.¹³³ Although some studies in the general population have reported inconsistent associations,¹³⁴ PTSD is formally recognized as a potential mental health sequela among family members of affected individuals, supporting the plausibility of this relationship.^135–137

Having a child role within the family was associated with a lower prevalence of PTSD, although this association was marginally significant. While direct evidence identifying this role as a protective factor against mental health disorders is limited, previous studies have shown that social connectedness and family support are associated with lower PTSD risk, whereas social isolation has been associated with increased vulnerability to posttraumatic symptoms.^32,123,138 This finding may reflect the protective effect of perceived familial belonging and support during crisis situations; however, given the limited available evidence, this association should be interpreted cautiously and warrants further investigation in future studies.

Students with subclinical, moderate, and severe insomnia exhibited a graded increase in PTSD prevalence. Additionally, this association is consistent with prior studies linking sleep disturbances—including short sleep duration, increased sleep latency, poor sleep quality, and nightmares—to greater PTSD severity across diverse populations^{123,139–142} In this context, evidence suggests a bidirectional relationship, whereby insomnia may both contribute to and result from PTSD.^143,144 Given this bidirectional association, interventions such as cognitive behavioral therapy for insomnia have been associated with reductions in PTSD-related psychiatric comorbidities,^145–148 potentially through mechanisms involving altered autonomic regulation during sleep.¹⁴⁹

The prevalence of PTSD increased by 78% among individuals with an eating disorder (ED). Previous evidence indicates that PTSD and eating disorders frequently co-occur, partly due to shared genetic vulnerability and overlapping neurobiological mechanisms.¹⁵⁰ PTSD has also been described as a common antecedent of eating disorders, with greater ED severity observed among individuals with comorbid PTSD.^151,152 Additionally, psychological distress appears to mediate the relationship between PTSD and ED severity.¹⁵³ These associations may be explained by trauma-related hyperarousal involving glutamatergic dysregulation, which affects brain circuits responsible for stress response and feeding regulation, including the hypothalamus, amygdala, and prefrontal cortex, thereby increasing susceptibility to PTSD.^153,154

Students with burnout syndrome showed a 34% higher prevalence of PTSD. This finding is consistent with previous studies indicating that academic burnout is associated with increased posttraumatic symptoms and negative mental health symptoms such as anxiety, depressive symptoms, emotional exhaustion, and psychological distress among students.^95,155,156 Similarly, some evidence suggests that stress may predict burnout rather than the reverse,¹⁵⁷ this variability in findings likely reflects a shared etiological pathway between burnout and PTSD, where exposure to sustained stress may initially lead to acute stress reactions and, if persistent, progress to PTSD.^158,159 In medical students, burnout represents a chronic and cumulative stressor that extends beyond acute events such as pandemics, contributing to long-term vulnerability to PTSD throughout medical training and subsequent professional practice.¹⁵⁶

Limitations and Strengths

Our study has limitations. First, the cross-sectional design of the study precludes identifying causal relationships between the variables of interest. Second, the period of data collection might have overestimated the level of resilience and underestimated the PTSD score.

Third, student self-reported data were used, so the responses may not have been entirely true, and the measurement of PTSD, resilience, and the other instruments of interest may have measurement bias due to the virtual nature of the data collection. Fourth, selection bias should be considered, as the sampling strategy was neither random nor proportional across all participating countries and university sites, a limitation commonly reported in previous studies assessing PTSD and resilience among medical students.^15,24–33 Consequently, the generalizability of our findings should be interpreted with caution. Although the study included medical students from multiple countries and institutions, the non-probabilistic snowball sampling strategy limits the extrapolation of results to all medical students in Latin America. Our findings are therefore more appropriately interpreted as providing regional and contextual insights rather than population-representative estimates.

Fifth, there is an unmeasured confounding bias, given that factors such as family dysfunctionality,¹⁶⁰ social support¹⁶¹ and stress markers such as cortisol have not been assessed in this research. Sixth, traumatic experiences were not explored in depth through a mixed-methods approach using in-depth interviews and/or focus groups.¹⁶²

However, this study also has several strengths. First, we captured a large population of medical students from 13 Latin American countries, covering all years of study. Second, we achieved a large sample size with broad geographic coverage. Due to the non-probabilistic snowball sampling strategy, a conventional response rate could not be calculated; however, the final sample size is comparable to or larger than that of similar multicenter studies conducted in this population. Third, we used validated questionnaires that provided data that can be compared with findings from other populations and an optimal data entry system. Fourth, it is important to note that, although our research does not establish causality, it offers a valuable approach to the potential protective role of resilience in mitigating the impact of COVID-19-derived PTSD in medical students; based on rigorous biostatistical methods.

Finally, to our knowledge, this study represents one of the first multicenter-level efforts that has explored the potential role of resilience in PTSD during the COVID-19 pandemic in Latin America, approached in a collaborative manner, providing a more representative perspective. It is relevant to note that, up to the date of this study, most of the research specifically between PTSD and resilience in the context of the pandemic has been conducted in settings other than the Latin American reality.^44,163 Our multicenter approach allows for a more complete understanding of the interaction between these factors in a specific context, considering the cultural and social particularities of the Latin American region.

Relevance of Mental Health Findings

The data provided in this study, collected during the COVID-19 pandemic, document the presence of PTSD symptoms among medical students in a context of widespread psychosocial stress. The level of stress experienced by each student has been overwhelming, and in this situation, resilience may represent an important coping resource. However, we emphasize that support from universities in the development of coping counseling is of vital importance. According to Holden et al,¹⁶⁴ there is a significant need for three important determinants: assessing the conditions that increase the risk of mental health disorders, developing strategies to mitigate the risk, and cultivating resilience as a protective factor against the development of adverse conditions such as PTSD. Firstly, we propose the implementation of mental health intervention programs. Government institutions should allocate resources for the necessary psychological support and provide financial grants to address PTSD in a timely manner. In education, it is essential to ensure a return to face-to-face education free of stigmatization of mental health disorders, creating an inclusive and bias-free educational environment. Medical students represent a particularly vulnerable group due to their early exposure to academic and clinical stressors, combined with limited professional autonomy and support, which may heighten their susceptibility to adverse mental health outcomes during and after large-scale crises such as the COVID-19 pandemic. These findings offer pioneering insight into mental health in response to an unforeseen situation such as the pandemic due to COVID-19. However, we understand that mental health deterioration can have a fluctuating course, so post-pandemic studies will be imperative to better understand the long-term needs of this group and tailor intervention strategies accordingly.

Consistent with this perspective, previous interventional studies in high-stress healthcare settings have shown that structured educational programs can strengthen coping capacities. For example, an experimental study among prehospital paramedic personnel in Iran demonstrated that bioethical principles education improved ethical attitudes, supporting the potential role of educational interventions in enhancing preparedness and resilience during crisis situations.¹⁶⁵

In addition, studies among healthcare professionals emphasize the importance of developing core competencies for disaster risk management, highlighting training, preparedness, and psychological readiness as key components.¹⁶⁶ Furthermore, qualitative evidence at the health system level indicates that organizational, managerial, and resource-related challenges during the COVID-19 pandemic substantially increased psychological strain among healthcare workers.¹⁶⁷ Addressing these structural factors is therefore essential for designing effective mental health and resilience interventions for medical students embedded within strained health systems.

Conclusions

The COVID-19 pandemic was associated with a substantial mental health burden among medical students, with approximately one in ten students presenting symptoms compatible with PTSD. We found a significant association between higher levels of resilience and a lower prevalence of PTSD, highlighting the protective role of resilience in this population. Additionally, younger age, specific socioeducational factors, and a prior history of mental health disorders were associated with a higher prevalence of PTSD symptoms.

These findings support the implementation of targeted strategies to strengthen resilience, the establishment of accessible psychological support services, and the integration of mental health education into medical curricula. Beyond the COVID-19 pandemic, our results provide valuable insights for addressing mental health challenges during future disasters and health crises. The strategies identified may serve as a guide for developing effective responses within academic institutions and healthcare systems facing similar large-scale emergencies. Investing in the mental health of medical students is likely to contribute to a more resilient medical workforce, better prepared to respond to future global health challenges.

Future studies should prioritize longitudinal designs to better assess the temporal relationship between resilience and PTSD symptoms among medical students, as well as interventional studies evaluating the effectiveness of resilience-building, ethical training, and disaster preparedness programs integrated into medical curricula. Additionally, qualitative and mixed-methods research may provide deeper insights into contextual and institutional factors influencing psychological outcomes during epidemics and disasters. Expanding multicenter studies to include diverse cultural and educational settings will further enhance the generalizability and applicability of future findings.

Supplemental Material

sj-docx-1-css-10.1177_24705470261430113 - Supplemental material for Post-traumatic Stress Disorder and Resilience in Medical Students: A Multicenter Study from 13 Latin American Countries

Supplemental material, sj-docx-1-css-10.1177_24705470261430113 for Post-traumatic Stress Disorder and Resilience in Medical Students: A Multicenter Study from 13 Latin American Countries by Mario J. Valladares-Garrido, J. Pierre Zila-Velasque, Ludwing A. Zeta Solis, David Astudillo Rueda, Renzo Acosta-Porzoliz, C. Ichiro Peralta Chiguala, Fatima Jiménez-Mozo, Christopher G. Valdiviezo-Morales, E. Sebastian Benavides Alburqueque, Estrella Christabel Porras Núñez, Helena Dominguez-Troncos and Víctor J. Vera-Ponce, Danai Valladares-Garrido, César J. Pereira-Victorio, Carlos Culquichicón, Oriana Rivera-Lozada, Cristian Diaz-Velez in Chronic Stress

Footnotes

Acknowledgments

Not applicable.

ORCID iDs

Mario J. Valladares-Garrido

Ludwing A. Zeta Solis

Renzo Acosta-Porzoliz

Estrella Christabel Porras Núñez

Ethical Considerations

This multicenter study was reviewed and approved by the COVID-19 Research Ethics Committee of the Social Health Insurance (EsSalud, Lima, Peru), which authorized the implementation of the protocol across universities in 13 Latin American countries. All procedures complied with the Declaration of Helsinki and its subsequent amendments.

Consent to Participate

Electronic informed consent was obtained from all participants prior to accessing the questionnaire. Data were collected anonymously using unique codes to protect confidentiality and privacy.

Consent for Publication

Not applicable.

Author Contributions

M.J. Valladares-Garrido: Conceptualization; Methodology; Investigation; Data curation; Writing—original draft; Writing—review & editing.

J.P. Zila-Velasque; L.A. Z.; D.A.R.; C.I. P.-C.; F. J.-M.; C.G. V.-M.; E.S. B. A.; E.C. P.-N.; H. D.-T.; V.J. V.-P.; D. Valladares-Garrido; V.E. F.-R.; C.J. P.-V.; C. C.; P.R. C.-U.: Methodology; Investigation; Writing—original draft; Writing—review & editing.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The publication fee (APC) was covered by Universidad Señor de Sipán (USS). M.J.V.-G. was supported by the Fogarty International Center of the National Institutes of Health (NIH), specifically the National Institute of Mental Health (NIMH), under Award Number D43TW009343, and by the University of California Global Health Institute.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

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Supplementary Material

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Post-traumatic Stress Disorder and Resilience in Medical Students: A Multicenter Study from 13 Latin American Countries

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Methods

Study Design

Population and Sample

Procedures

Instruments

Variables

Data Analysis

Ethical Aspects

Results

Characteristics of the Study Sample

Bivariate Analysis Between post-Traumatic Stress and Resilience, and Other Associated Factors

Association Between Posttraumatic Stress and Resilience in Simple and Multiple Regression Analysis

Other Factors Associated with Posttraumatic Stress in Simple and Multiple Regression Analysis

Discussion

Prevalence of PTSD

Prevalence of Resilience

Association Between post-Traumatic Stress and Resilience

Other Factors Associated with PTSD

Limitations and Strengths

Relevance of Mental Health Findings

Conclusions

Supplemental Material

sj-docx-1-css-10.1177_24705470261430113 - Supplemental material for Post-traumatic Stress Disorder and Resilience in Medical Students: A Multicenter Study from 13 Latin American Countries

Footnotes

Acknowledgments

ORCID iDs

Ethical Considerations

Consent to Participate

Consent for Publication

Author Contributions

Funding

Declaration of Conflicting Interests

Supplemental Material

References

Supplementary Material