Disaster readiness and resilience among emergency medical service providers: a cross-sectional study in Iran

Abstract

Background

Pre-hospital Emergency Medical Services (EMS) providers serve a critical function in disaster response. While technical preparedness remains essential, psychological resilience is equally vital for sustaining performance amidst stressful conditions. This research endeavored to assess disaster readiness and resilience among EMS providers in Tehran Province, Iran, and to identify the factors associated with resilience.

Methods

A cross-sectional analytical study was conducted in 2025, involving 467 pre-hospital EMS providers, using online self-administered questionnaires. Data collection encompassed demographics, Persian Version of the Disaster Nursing Readiness Evaluation Index (F-DNREI), and Disaster Resilience Measurement Tool. Descriptive statistics, correlation analyses, and multiple linear regression were employed to identify factors associated with resilience.

Results

Participants’ mean disaster readiness score was 95.88 ± 10.62, whereas resilience mean score was 44.81 ± 9.50, with only 3.85% classified as highly resilient. Resilience was significantly correlated with readiness (r = 0.562, p < 0.001) and was positively associated with work experience, higher education, and prior attendance at a disaster management workshop. In the final multivariable regression model, age was excluded due to near-perfect collinearity with work experience, disaster readiness, holding a bachelor’s degree, and prior workshop participation; these variables were retained as independent predictors of disaster resilience (R² = 0.606).

Conclusions

Despite adequate disaster readiness, resilience was low, indicating a critical gap between technical preparedness and psychological adaptability. Education, experience, and prior training emerged as significant factors associated with resilience. Based on these observational associations, integrating simulation-based training with resilience-enhancement and psychosocial-support components warrants evaluation as a potential strategy to strengthen both readiness and adaptive capacity. Future research should examine these dynamics in real emergency scenarios.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-026-04917-4.

Introduction

Emergency medical services (EMS) serve as a crucial link between the public and healthcare facilities, such as hospitals [1]. EMS are on the front line of providing care during emergencies and disasters, whether natural or man-made, as well as for the treatment of critical medical conditions [2].

Disasters arise when hazardous events interact with vulnerability and exposure, disrupting community functioning, and are broadly classified as natural or human-made [3, 4]. Due to its geographical characteristics, Iran experiences a range of natural events, including earthquakes, floods, and storms, as well as human-induced incidents, such as road traffic accidents and air crashes [5, 6].

Given the widespread occurrence of disasters worldwide, crisis management must operate at maximum effectiveness [7]. This can be achieved by strengthening the four key phases of disaster management: prevention and mitigation, preparedness, response, and recovery [8]. Preparedness refers to the ability of governments, response and recovery professionals, and communities, both individuals and groups, to anticipate, respond to, and recover from imminent, potential, or ongoing hazard events [9].

Having skilled and knowledgeable personnel in the health system is essential, as they are key to crisis management [9, 10]. Emergency medical staff, especially those providing pre-hospital care, need proper training in crisis management and disaster preparedness [10]. Factors influencing disaster readiness include education, drills, management, guidelines, standards, equipment, and structural layout. Among these, training through exercises and simulations is the most crucial for enhancing readiness and improving disaster and emergency response [7, 11]. In addition, improving individual disaster readiness not only enhances crisis management but also builds greater resilience at both the personal and community levels [12].

Resilience refers to the capacity of individuals, groups, organizations, or entire populations to quickly recover from psychological or behavioral disruptions caused by major incidents, terrorism, or collective disasters [13]. Given that pre-hospital EMS teams frequently handle emergencies, resilience is critically essential in pre-hospital emergency care [14].

A study conducted in Thailand found that EMS providers demonstrated moderate resilience in behavioral health, safety components, well-being, and competencies [15]. The Connor–Davidson Resilience Scale (2003) conceptualises resilience in terms of five components: perception of personal competence, trust in one’s instincts, tolerance of negative affect, positive acceptance of change, and secure relationships and spiritual influences [16].

Resilience is affected by various factors [17]. A 2020 systematic review of psychological interventions aimed at improving resilience among healthcare workers showed that resilience training significantly increased resilience scores and substantially reduced symptoms of depression and stress, serving as a secondary prevention approach for behavioral health problems [18]. A recent study indicates that healthcare personnel can be shielded from adverse emotional outcomes by enhancing workplace support and reducing occupational stress [19].

Despite growing interest in disaster preparedness, relatively few studies have simultaneously quantified disaster readiness and psychological resilience among pre-hospital EMS providers, particularly in Middle Eastern settings, leaving uncertainty about the extent of alignment or mismatch between technical readiness and adaptive psychological capacity in this workforce.

This investigation was guided primarily by the Transactional Model of Stress and Coping [20], which frames disaster readiness as reflecting the appraisal of demands and available procedural resources, and resilience as reflecting coping and adaptive capacity. Person–Environment Fit Theory and Ecological Systems Theory are used as complementary lenses for interpreting individual–environmental and multilevel influences [21, 22].

Given the importance of EMS providers’ readiness to respond to disasters, which can affect the effectiveness of crisis management, it is necessary to assess readiness to inform improvement strategies. Moreover, because readiness affects resilience, it is imperative to determine their resilience. Given the pivotal role of pre-hospital EMS providers in disaster response, this study aimed to assess the levels of disaster readiness and resilience.

Methods

Study design

This cross-sectional analytical study was conducted among prehospital EMS providers in Tehran Province, Iran, in 2025. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Also, the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) was adhered to ensure transparency and reliability in web-based data collection [23, 24] (Supplementary Materials 1).

Setting and participants

This study was conducted within the Tehran EMS system, which operates under the national EMS framework administered by Iran’s Ministry of Health and Medical Education (MOHME). The Tehran EMS network delivers prehospital emergency care across 16 counties in Tehran Province, the country’s capital region, through 295 operational bases comprising urban (city), road (inter-urban highway), and aerial (helicopter-based) units. At the time of data collection, approximately 2,458 emergency medical technicians (EMTs) were employed across these bases. For administrative efficiency and equitable service distribution, Tehran EMS is organised into nine operational regions that coordinate prehospital emergency care across the entire province.

In Iran, prehospital EMS is a nationally coordinated system under MOHME, responsible not only for ambulance services but also for policy development, planning, intersectoral coordination, and oversight of medical disaster management. The national EMS serves as the frontline component of Iran’s disaster response structure, working closely with the Iranian Red Crescent Society, hospital-based emergency departments, and other government agencies. EMS personnel play a central role across all phases of disaster management, including preparedness, mitigation, response, and recovery [25].

EMTs typically enter the workforce through associate- or bachelor-level programs in emergency medicine, nursing, or anesthesia. National EMS standards emphasise the development of a skilled and motivated workforce, supported by ongoing training, operational quality improvement, research, and the integration of modern technologies. The scope of EMS practice includes providing prehospital emergency care, trauma management, medical stabilisation, triage, and coordination of patient transport during routine incidents and large-scale emergencies [5, 7, 8].

This study included all eligible prehospital EMS providers working in Tehran Province during the study period.

Eligibility criteria

Participants were eligible if they (i) were pre-hospital EMTs employed at Tehran EMS bases across urban (city), road (inter-urban highway), and aerial (helicopter-based) units; (ii) had at least six months of full-time experience; and (iii) held at least an associate degree in emergency medicine, nursing, or anesthesia. Technicians who were on administrative leave at the time of data collection, or who did not complete the online questionnaire in full, were excluded. The six months minimum was adopted to ensure that participants had completed the standard probationary period and had sufficient operational exposure to meaningfully self-rate disaster readiness and resilience.

Sample size

The sample size was determined using G*Power (version 3.1) for a linear multiple regression, based on the R² deviation from zero [26]. Based on a similar study [27], the observed R² was 0.383, which corresponds to an effect size (f²) of 0.62. With a significance level (α) of 0.05, a power of 0.95, and 7 predictors, the minimum sample size needed was 43 participants. A power of 0.95 was adopted because (i) online recruitment through official channels made a large sample readily attainable at negligible incremental cost, and (ii) the multivariable regression model included multiple candidate predictors, for which higher power reduces the risk of Type II error for smaller effects. A sensitivity analysis assessed how different effect sizes might influence the required sample size. The calculated sample was about 153 participants for a medium effect (f² = 0.15) [28]. Also, 444 participants were needed for a near small effect (f² = 0.05). These findings suggest that the selected sample size is adequate for identifying large and medium effects and detecting some small effects with sufficient power, while minimizing data loss or errors. After accounting for an expected 10% attrition, the final sample size was approximately 488 participants.

Data collection procedures

Data were gathered online via the https://porsline.ir/ platform from August 30 to November 6, 2025. With approval from the Tehran EMS director, the director and deputy briefed regional supervisors on the study aims, questionnaire details, and completion guidelines. Supervisors then shared the survey link through official pre-hospital EMS channels, including internal messaging, Bale, WhatsApp, and Telegram groups, to reach technicians across all nine administrative regions. The survey was restricted and not publicly advertised; the link was distributed only via official channels to regional supervisors, who forwarded it to eligible technicians. Invitations were not personalised; only one response per IP address was permitted. All items were mandatory; participants could not submit the questionnaire with missing items. No skip logic or adaptive questioning was used. Items were displayed with approximately one item per page, and a progress bar was shown. Investigators tracked responses in real time, sent two reminders at 48-hour intervals through the same channels to boost participation, and logged participant flow, covering invitations, link visits, consents, and completions, per the CHERRIES checklist. Technical protocols were implemented to ensure data quality. Only one response per IP address was allowed, and timestamp and response-pattern checks identified invalid or automated entries during data cleaning. Only fully completed questionnaires were included in the final analysis. Submitted questionnaires were screened for implausible response patterns by employing two predefined criteria: (i) straight-lining, defined as providing identical responses to ≥ 90% of items within a subscale; and (ii) a completion time falling below the 1st percentile of the observed time distribution. To encourage engagement, regional supervisors provided official support and periodic reminders, and participants received an executive summary of the study findings after completion. No monetary incentives were offered. All data exports were securely stored on password-protected institutional servers, with access limited to the research team.

Instruments

This study employed three instruments for data collection, detailed as follows:

Demographic information questionnaire

This questionnaire collected participants’ sociodemographic and occupational information, including age, marital status, employment status, work experience, average monthly shifts, shift pattern, financial status, education level, and involvement in crisis management training programs designed to enhance disaster readiness and resilience.

Persian version of the Disaster Nursing Readiness Evaluation Index (F-DNREI)

Data were collected using the Farsi version of the Disaster Nursing Readiness Evaluation Index, developed by Maeda et al. (2018) in Japan [29]. Gheshlagh et al. (2024) psychometrically validated the Persian version of the Disaster Nursing Readiness Evaluation Index. Although the instrument was originally developed for nurses, its items cover disaster-site adaptability, practical response skills, teamwork and communication, emergency clinical skills, and stress-coping, competencies that are operationally shared between hospital nurses and pre-hospital EMS providers in Iran, where eligibility for EMS roles includes nursing and anaesthesia backgrounds. Many EMTs obtain their qualifications through nursing or anaesthesia programs, and their clinical duties, decision-making responsibilities, and disaster-related functions overlap substantially with those of nurses. Given these parallels in competency domains and professional roles, the conceptual constructs assessed by the F-DNREI are considered applicable to pre-hospital EMS providers within the Iranian EMS system. The Farsi version includes 32 items divided into five subscales: Adaptability to stressful situations at the disaster site, Practical skills for disaster response, Communication and cooperation skills for teamwork, Emergency nursing skills, and Effective coping with daily stress. Items are rated on a 5-point Likert scale from 1 (“strongly disagree”) to 5 (“strongly agree”). Exploratory factor analysis (EFA) revealed a five-factor structure, while confirmatory factor analysis (CFA) confirmed a good model fit [30]. The scale’s reliability was high, with Cronbach’s alpha at 0.89 and McDonald’s omega at 0.891 [30]. In this study, reliability was evaluated using Cronbach’s alpha for the full analytic sample (α = 0.78) (Supplementary Materials 2).

Disaster resilience measurement tool

The Disaster Resilience Measurement Tool was initially developed by Mao et al. [31] and later translated into Farsi, with psychometric validation, by Shabani et al. [32]. It includes 19 items across four subscales: self-efficacy, social support, positive growth, and altruism. Participants rate items on a 4-point Likert scale (1 = “strongly disagree” to 4 = “strongly agree”), with higher scores indicating greater resilience. The original version showed good internal consistency (Cronbach’s α = 0.84) and test–retest reliability over two weeks (ICC = 0.85) [31]. A cutoff score of 61 distinguishes high from low resilience levels. For the Farsi version, the Scale Content Validity Ratio (S-CVR) was 0.92, and the Scale Content Validity Index (S-CVI) was 0.93; the scale’s comprehensiveness was 0.875%, Cronbach’s α was 0.89, and ICC ranged from 0.68 to 0.92. Exploratory factor analysis revealed four factors, accounting for 54.58% of the total variance [32]. In this study, reliability was assessed using Cronbach’s alpha, which was 0.90 for the full analytic sample (Supplementary Materials 3).

Ethical considerations

This study received approval from the Ethics Committee of Shahid Beheshti University of Medical Sciences in Tehran, Iran (Ethics Code: IR.SBMU.RETECH.REC.1404.345). It was conducted in accordance with the ethical principles of the Declaration of Helsinki. All participants were informed about the study’s goals, procedures, risks, and benefits before taking part. Informed consent was obtained electronically via the online survey platform, and participation was entirely voluntary. Electronic informed consent was obtained via a mandatory checkbox on the initial page of the online survey, which participants were required to select to proceed to the questionnaire items. Participants also retained the option to terminate the survey at any point by closing the browser window. Participants were assured of the confidentiality and anonymity of their responses, and no personal identifying information was collected. They retained the right to withdraw at any time without penalty. Data were securely stored, and access was restricted to the principal investigators solely for research purposes.

Data analysis

Before analysis, the data were cleaned using the following steps: (i) verification that all mandatory items were completed; (ii) removal of duplicate entries using IP-address deduplication; and (iii) application of the two response-quality rules described above (straight-lining and implausible speed).

Participant demographic characteristics were summarized using means and standard deviations for continuous variables, and frequencies and percentages for categorical variables. The normality of key study variables was evaluated through the Shapiro–Wilk test, skewness and kurtosis measures, Q–Q plots, and histograms (Supplementary Material 4). Bivariate relationships between the primary variables and continuous demographic factors were examined using Pearson’s correlation. Univariate analyses involved independent-samples t-tests for dichotomous variables and one-way ANOVA for variables with multiple categories. Multiple linear regression was then performed with disaster resilience as the dependent variable. The final multivariable model included the following candidate variables: disaster readiness (total score), work experience (years), education level (associate vs. bachelor), prior attendance at a disaster-management workshop (yes/no), and other demographic variables that were significant in univariate analysis (p < 0.10). Age was considered but excluded a priori because of its near-perfect collinearity with work experience (r = 0.992). Multicollinearity among candidate predictors was assessed using the variance inflation factor (VIF) and tolerance, with VIF ≥ 5 considered indicative of problematic collinearity; when two variables were near-collinear, the more theoretically proximal variable was retained. Linear-regression assumptions, including linearity, independence of errors, homoscedasticity, normality of residuals, and absence of influential outliers, were examined prior to interpretation. A p-value below 0.05 was considered statistically significant.

Results

The web-survey link was distributed via supervisor-mediated channels to 488 eligible EMTs. Of these, 467 (95.7%) accessed the link, provided electronic informed consent, completed all mandatory items, and were included in the final analytic sample. Twenty-one EMTs (4.3%) were excluded because they did not access the link or were flagged by the pre-specified data-quality rules.

Sample characteristics

Most participants were married and generally described their financial status as average, while smaller proportions reported either good or poor financial conditions. Educationally, the group was predominantly composed of individuals with bachelor’s degrees, with the remainder having completed associate-level education.

In terms of work-related attributes, participants were distributed across various shift patterns, with long and night shifts comprising the largest groups, alongside smaller but notable proportions working full or rotating shifts. Employment arrangements also varied, with formal positions most common, supplemented by contract-based, temporary, and other forms of employment. A substantial portion of the sample had prior exposure to disaster-management training through previous workshops.

The mean age of the sample was 34.39 ± 7.47 years (range 22–55), the mean work experience was 11.54 ± 7.43 years (range 0–33), and participants worked an average of 18.27 ± 5.16 shifts per month (range 4–30, Table 1).

Table 1.

Demographic characteristics of included participants (n = 467)

Categorical Variable		n (%)
Marital Status	Married	312 (66.8)
	Single	140 (30.0)
	Other	15 (3.2)
	Total	467 (100)
Financial Status	Average	288 (61.7)
	Good	115 (24.6)
	Poor	64 (13.7)
	Total	467 (100)
Education Level	Bachelor	409 (87.6)
	Associate	58 (12.4)
	Total	467 (100)
Shift Pattern	Long	190 (40.7)
	Night	182 (39.0)
	Full	70 (15.0)
	Rotate	25 (5.3)
	Total	467 (100)
Employment status	Formal	257 (55.0)
	Contract	109 (23.4)
	Temporary	79 (16.9)
	Other	22 (4.7)
	Total	467 (100)
Previous Workshop on Disaster Management	Yes	284 (60.8)
	No	183 (39.2)
	Total	467 (100)
Continuous Variables	Mean ± SD	Range
Age (Years)	34.39 ± 7.47	(22–55)
Work experience (Years)	11.54 ± 7.43	(0–33)
Average shifts per month	18.27 ± 5.16	(4–30)

Descriptive statistics for main variables

Disaster resilience

Participants’ overall disaster resilience score was 44.81 ± 9.50 (N = 467). Subscale means included: Altruism 7.48 ± 1.96, Social Support 10.01 ± 2.46, Self-efficacy 17.40 ± 4.16, and Positive Growth 12.45 ± 2.81. Most participants were classified as having low resilience (n = 449, 96.15%), while only 18 (3.85%) were classified as having high resilience (Table 2).

Table 2.

Descriptive statistics for main study variables (N= 467)

Variable		Frequency (n)	Percentage (%)	Mean	SD
Disaster Resilience	Total	467	100	44.809	9.495
	Altruism	467	100	7.484	1.962
	Social support	467	100	10.009	2.464
	Self-efficacy	467	100	17.398	4.16
	Positive growth	467	100	12.445	2.814
Low Resilience		449	96.15
High Resilience		18	3.85
Disaster Readiness	Total	467	100	95.88	10.622
	Adaptability to stressful situations at the disaster site	467	100	24.062	3.956
	Practical skills for disaster response	467	100	20.964	3.73
	Communication and cooperation skills for teamwork	467	100	27.002	4.451
	Emergency nursing skills	467	100	17.889	3.095
	Effective coping with daily stress	467	100	5.964	1.442

Disaster readiness

The overall disaster readiness score averaged 95.88 ± 10.62 (N = 467). Subscale means were: Adaptability to stressful situations at the disaster site 24.06 ± 3.96, Practical skills for disaster response 20.96 ± 3.73, Communication and cooperation skills for teamwork 27.00 ± 4.45, Emergency nursing skills 17.89 ± 3.10, and effective coping with daily stress 5.96 ± 1.44 (Table 2).

Correlations among disaster resilience, disaster readiness, and participant characteristics

Strong, positive correlations were observed within resilience and its subscales (self-efficacy r = 0.931; positive growth r = 0.861; social support r = 0.852; altruism r = 0.791; all p < 0.001). Resilience showed a moderate correlation with disaster readiness (r = 0.562, p < 0.001); the readiness subdomains demonstrated small to moderate links with resilience (communication: r = 0.420; adaptability: r = 0.414). Age and work experience were moderately correlated with resilience (r = 0.44–0.46), but they were almost perfectly correlated (r = 0.992), so only one was included in the multivariable model to prevent collinearity. Average monthly shifts had minimal associations with the primary outcomes (r = 0.069, p = 0.135, Table 3).

Table 3.

Bivariate analysis to estimate correlation coefficients (N = 467)

Variables		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
(1) Disaster Resilience		1.000
	p-value
(2) Altruism		0.791	1.000
	p-value	(< 0.001)
(3) Social support		0.852	0.640	1.000
	p-value	(< 0.001)	(< 0.001)
(4) Self-efficacy		0.931	0.695	0.708	1.000
	p-value	(< 0.001)	(< 0.001)	(< 0.001)
(5) Positive growth		0.861	0.604	0.653	0.709	1.000
	p-value	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)
(6) Disaster Readiness		0.562	0.449	0.470	0.516	0.490	1.000
	p-value	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)
(7) Adaptability		0.414	0.321	0.323	0.390	0.372	0.646	1.000
	p-value	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)
(8) Practical		0.309	0.242	0.297	0.281	0.243	0.657	0.259	1.000
	p-value	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)
(9) Communication		0.420	0.343	0.356	0.372	0.374	0.703	0.215	0.274	1.000
	p-value	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)
(10) Emergency skills		0.335	0.270	0.260	0.314	0.304	0.595	0.235	0.200	0.273	1.000
	p-value	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)
(11) Effective coping		0.187	0.166	0.146	0.182	0.153	0.448	0.176	0.264	0.207	0.229	1.000
	p-value	(< 0.001)	(< 0.001)	(0.002)	(< 0.001)	(0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)
(12) Age		0.440	0.394	0.362	0.411	0.370	-0.037	-0.018	-0.072	-0.016	0.021	-0.031	1.000
	p-value	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(0.425)	(0.702)	(0.119)	(0.722)	(0.651)	(0.507)
(13) Work experience		0.456	0.406	0.374	0.426	0.384	-0.030	-0.012	-0.075	-0.004	0.019	-0.024	0.992	1.000
	p-value	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(< 0.001)	(0.513)	(0.789)	(0.104)	(0.935)	(0.678)	(0.602)	(< 0.001)
(14) Average shifts		0.069	-0.006	0.124	0.055	0.067	0.080	0.094	0.058	< 0.001	0.063	0.048	-0.017	-0.020	1.000
	p-value	(0.135)	(0.889)	(0.007)	(0.233)	(0.147)	(0.083)	(0.042)	(0.208)	(1.000)	(0.175)	(0.300)	(0.712)	(0.664)

Univariate analysis of disaster readiness and resilience

Educational level was linked to both outcomes: participants with an associate degree reported higher disaster readiness (mean = 98.48 ± 10.51) but lower disaster resilience (mean = 41.28 ± 8.87) compared to those with a bachelor’s degree (readiness = 95.51 ± 10.60; resilience = 45.31 ± 9.49); these differences were statistically significant for readiness (p = 0.046) and resilience (p = 0.003). Attending a previous disaster management workshop was associated with significantly greater resilience (Yes: 46.47 ± 9.18 vs. No: 42.23 ± 9.43; p < 0.001), but not with readiness (p = 0.851). No significant differences in disaster readiness or resilience were found based on marital status, financial status, shift pattern, or employment status (all p > 0.05, Table 4).

Table 4.

Univariate analysis of disaster readiness and resilience by sample characteristics (N = 467)

Characteristics	N	Disaster Resilience		P value	Disaster Readiness		P value
		Mean	SD
Marital Status
Married	312	44.657	9.517	0.572 a	96.128	10.806	0.737 a
Single	140	45.35	9.613		95.293	10.456
Other	15	42.933	8.04		96.2	8.428
Educational Level
Associate	58	41.276	8.865	0.003 b	98.483	10.51	0.046 b
Bachelor	409	45.311	9.485		95.511	10.598
Financial Status
Average	288	44.809	9.689	0.211 a	95.774	10.615	0.466 a
Good	115	45.748	9.463		96.765	9.744
Poor	64	43.125	8.527		94.766	12.11
Shift Pattern
Long	190	45.268	9.399	0.511 a	96.679	10.999	0.215 a
Night	182	44.879	9.437		96.071	10.356
Full	70	44.243	9.887		94.114	10.129
Rotate	25	42.4	9.674		93.36	10.634
Employment Status
Formal	257	44.848	10.045	0.209 a	95.685	10.989	0.150 a
Contract	109	45.615	9.137		96.468	10.001
Temporary	79	44.658	8.521		96.975	10.163
Other	22	40.909	7.224		91.318	10.232
Previous Workshop on Disaster Management
Yes	284	46.472	9.176	< 0.001 b	95.954	10.61	0.851 b
No	183	42.23	9.431		95.765	10.668

^aAnalysis of variance (ANOVA)

^bIndependent t-tests

Factors associated with disaster resilience

Multivariate linear regression analysis (Table 5), with age excluded a priori because of its near-perfect correlation with work experience (r = 0.992) and the resulting multicollinearity, identified disaster readiness, work experience, educational level, and prior disaster management workshop attendance as significant factors associated with disaster resilience, (adjusted R² = 0.6059; overall model p < 0.001; VIF < 5). Higher disaster readiness (β = 0.589, p < 0.001) and greater work experience (β = 0.461, p < 0.001) were the strongest positive factors associated with resilience, followed by previous workshop attendance (β = 0.206, p < 0.001) and having a bachelor’s degree (β= 0.166, p < 0.001). Unstandardised B, Standardised β coefficients, standard errors, and 95% confidence intervals (CIs) are presented in revised Table 5.

Table 5.

Multivariate analysis of contributing factors to disaster resilience. F (4, 462) = 180.08, Prob > F = 0.000, R-squared 0.6092, Adj R-squared 0.6059

Variable		B	β	Std. error	t	P>|t|	95% Confidence Interval		VIF
							LL	UL
Disaster Readiness		0.527	0.589	0.026	20.16	< 0.001	0.475	0.578	1.01
Work experience (Years)		0.589	0.461	0.037	15.81	< 0.001	0.516	0.662	1.00
Educational Level	Bachelor	4.772	0.166	0.841	5.67	< 0.001	3.119	6.426	1.01
Previous Workshop on Disaster Management	Yes	4.008	0.206	0.565	7.09	< 0.001	2.898	5.119	1.00
Constant		-19.19	-	2.743	-6.96	< 0.001	-24.49	-13.71	-
Assumptions		Normality of Residuals Shapiro–Wilk W test: p = 0.456 Breusch–Pagan/Cook–Weisberg test for heteroskedasticity: p = 0.382

UL Upper Limit, LL Lower Limit

Discussion

This study aimed to assess disaster preparedness and resilience among pre-hospital EMS providers in Tehran Province and to identify determinants of resilience. In a convenience sample, self-reported disaster readiness was relatively maintained, whereas overall disaster resilience was low. In multivariable analysis, greater readiness, longer work experience, possession of a bachelor’s degree, and prior attendance at a disaster-management workshop independently predicted higher resilience.

Although participants showed relatively high levels of disaster preparedness, their resilience scores stayed low, indicating a possible gap between technical skills and psychological flexibility. Consistent with the primary conceptual framework adopted in this study, this difference can be explained through the Transactional Model of Stress and Coping (Lazarus & Folkman, 1984), which states that people assess stressful situations based on perceived demands and available coping resources [20]. While readiness primarily reflects mental and procedural aspects, such as skills, knowledge, and familiarity with protocols, resilience encompasses emotional stability, adaptive coping strategies, and sustained motivation during challenging times. Therefore, even if EMS personnel are technically ready, ongoing occupational stress, heavy workloads, and limited psychosocial support may constrain their ability to perform effectively during prolonged emergencies. The positive impact of higher education on resilience might come from improved cognitive flexibility, self-awareness, and self-regulation, which help EMS workers better interpret and handle complex or unclear disaster scenarios. Likewise, more work experience supports learning through practice, leading to becoming accustomed to stressful environments and developing intuitive decision-making. This pattern aligns with Person–Environment Fit Theory, which suggests that people experience less stress and perform better when their abilities match the demands of their environment. In this context, experienced and well-trained EMS technicians may better align their skills with the challenges of prehospital emergency care, thus strengthening their resilience [22, 33–35].

The proportion of participants classified as highly resilient (3.85%) warrants cautious interpretation. The cutoff used for this classification was derived from Mao et al.‘s original development sample of Chinese healthcare rescuers [31], and the Persian psychometric validation [31] does not report an EMS-specific threshold. Cultural response tendencies in Likert scoring, language-specific framing, and the distinct occupational context of Iranian pre-hospital EMS may all affect the observed score distribution. Accordingly, the “high resilience” figure should be read as a tool-referenced classification rather than an absolute population-level estimate.

The regression model’s explanatory power should also be interpreted cautiously. Although an R² of this magnitude is not implausible for models that combine a strongly correlated construct with demographic and experiential predictors, some of the explained variance may reflect conceptual overlap between the readiness and resilience constructs rather than independent effects. Overfitting cannot be ruled out, and cross-validation in an independent sample is recommended.

Beyrami Jam et al. (2021) conducted a national cross-sectional study to evaluate the disaster preparedness of EMS facilities across Iran. They reported that most EMS centers demonstrated moderate readiness, with notable deficiencies in coordination, cooperation, and surge capacity [8]. Similarly, Dahmashy et al. (2024) assessed paramedics in Saudi Arabia. They found moderate-to-high preparedness levels, particularly in triage and evacuation knowledge, while limited awareness of hospital disaster plans and insufficient inter-agency coordination were observed [36]. Along the same lines, Rishipathak et al. (2021) in India found that EMS professionals exhibited suboptimal awareness and operational preparedness for disaster management, underscoring the urgent need for ongoing training and feedback mechanisms [37]. Together, these findings align with the present study, which demonstrated that although disaster preparedness scores among Tehran pre-hospital technicians were relatively favorable, overall resilience levels were low. This parallel trend across multiple Asian contexts may be attributed to shared systemic challenges, including inadequate structured drills, inconsistent national preparedness frameworks, and limited interdisciplinary coordination. However, the relatively higher readiness score observed in the present study may reflect the specific emphasis placed by Tehran EMS authorities on periodic refresher courses and structured emergency response drills, particularly following the COVID-19 pandemic, which heightened institutional awareness of large-scale emergencies.

Pant et al. (2024) examined the resilience capacity of pre-hospital EMS providers nationwide in Thailand and found moderate resilience in the domains of safety, wellness, and behavioral health [15]. Likewise, Huang et al. (2022) explored resilience, coping style, and psychological distress among rescue nurses on national EMTs in China, reporting that resilience was significantly associated with positive coping and inversely related to anxiety and depression [38]. These studies converge with the current findings, which indicate that disaster resilience among Iranian EMS technicians was low to moderate, suggesting that psychosocial and environmental factors remain central determinants of EMS personnel’s ability to withstand stress and maintain functionality under pressure. The marginally lower resilience scores in this study may be due to increased operational load and reduced mental health support available to Iranian EMS providers, as well as sociocultural factors that affect self-efficacy and perceived stress. Previous evidence suggests that organizational support, peer relationships, and perceived control significantly enhance resilience [39], and the limited institutional resources within the Tehran EMS structure may have constrained these protective mechanisms.

Mantas-Jiménez et al. (2022) further investigated resilience and job satisfaction among out-of-hospital EMS professionals. They reported that higher resilience was positively associated with job satisfaction and collaborative work [39]. The current study partially supports this association: participants with greater educational attainment and prior workshop participation demonstrated higher resilience, suggesting that both professional competence and workplace culture contribute to adaptive capacity. Consistent with these observations, prior studies have emphasized that resilience enhancement requires an integrative approach that combines psychosocial support, continuing education, and team-based operational models [40, 41].

In addition, Al Razeeni et al. (2021) evaluated preparedness among Saudi EMS students during the COVID-19 pandemic. They revealed moderate deficiencies in both theoretical and practical competencies, with participants expressing the need for embedded disaster-related curricula and simulation-based training [42]. This resonates with the present study’s finding that prior participation in disaster management workshops was a strong predictor of greater resilience. Such convergence underscores the universal importance of structured education and scenario-based learning in fostering preparedness and adaptive coping. Differences in magnitude between studies may stem from the professional maturity of the target groups, students versus practicing technicians, and the degree of experiential learning and field exposure each group possesses. Previous literature confirms that experiential exposure to real incidents enhances cognitive readiness, confidence, and resilience more effectively than theoretical instruction alone [43, 44]. Evidence from developed settings, indicates that emergency responders exhibit markedly higher resilience. These differences likely reflect stronger institutional frameworks for psychosocial support, structured debriefing systems, and mandated resilience training as part of continuing professional development. Moreover, EMS organizations in high-resource contexts typically implement regular simulation exercises, peer-support networks, and confidential counseling services, which together foster a more sustainable culture of resilience. Conversely, resource constraints, limited mental health infrastructure, and sociocultural stigmas around emotional expression in Iran may collectively contribute to diminished resilience among EMS personnel, despite adequate technical preparedness [40].

Collectively, these comparative findings indicate that disaster preparedness and resilience among EMS providers are influenced by a complex interplay of individual, organizational, and contextual variables. The current study’s identification of work experience, educational level, and prior disaster-training exposure as predictors of resilience aligns with evidence from cross-national studies suggesting that resilience is both teachable and organizationally modifiable. Nonetheless, the generally lower resilience levels observed in this study may reflect systemic limitations in resource allocation, insufficient psychosocial support structures, and limited incorporation of resilience-based training in formal EMS curricula. Addressing these issues requires targeted policies that promote staff well-being, simulation-based education, and intersectoral collaboration to strengthen both the psychological and operational components of disaster preparedness in the pre-hospital setting.

Taken together, the findings of this study support an integrated view of resilience as both a personal and systemic concept. In line with Ecological Systems Theory, resilience among EMS personnel results from dynamic interactions among individual traits, organizational culture, and the broader socio-political environment. Therefore, simply improving disaster preparedness is not enough unless it is paired with organizational efforts that boost emotional regulation, social connections, and trust within institutions. From a policy standpoint, incorporating resilience training, structured peer support, and mental health resources into Iran’s EMS system could be an effective long-term strategy to close the gap between readiness and effective emergency response [21].

Strengths

The present study has several methodological and conceptual strengths. It is one of the few investigations in Iran to simultaneously examine both disaster preparedness and resilience among pre-hospital EMTs, using validated, psychometrically sound Persian instruments. By following the STROBE and CHERRIES guidelines, the study ensured methodological transparency. It reduced information bias through rigorous data quality controls, including IP restriction, mandatory responses, and exclusion of incomplete questionnaires. The large sample size and coverage across nine administrative regions throughout the province improved the representativeness and generalizability of the findings. Furthermore, incorporating demographic, educational, and experiential factors into a multivariate framework enabled a detailed understanding of the predictors of disaster resilience, offering new empirical evidence for an area of emergency preparedness research that remains relatively underexplored in the Middle East.

Limitations

Despite these strengths, some limitations should be recognized. First, the cross-sectional design precludes establishing causal directionality between readiness and resilience, and the temporal ordering between appraised demands and coping resources cannot be inferred from the present data; longitudinal studies could better clarify the timing of these factors. Second, data were collected via self-administered online questionnaires, which might have introduced reporting bias due to social desirability or self-perception biases. Third, although the sample was geographically diverse, it was limited to EMS providers in Tehran Province, which may limit the extent to which the results apply to other regions with different infrastructures or resources. Additionally, unmeasured psychosocial variables, such as personality traits, organizational culture, and coping mechanisms, together with unmeasured organisational variables such as leadership support, team cohesion, and perceived managerial responsiveness, may have affected resilience but were not included in this analysis.

Further, several additional limitations should be noted. Survey dissemination via institutional messaging platforms (Bale, WhatsApp, Telegram) may have selectively favoured participants who are more engaged with digital channels. Supervisor-mediated distribution of the survey link, although it supported coverage, may have created response pressure and thereby introduced a form of response bias. The high-resilience cutoff was originally developed in a non-EMS, non-Iranian sample, and the 3.85% classification should therefore be interpreted as tool-referenced rather than population-referenced; cultural response tendencies in Persian-language Likert scoring may further shape score distributions. The F-DNREI was originally developed for nurses; although its operational domain overlaps with pre-hospital EMS practice, its cross-population use remains a conceptual limitation. The short data-collection window, while operationally feasible, may further amplify social-desirability and response-pattern effects that a longer window could attenuate. Data were collected under routine, non-disaster conditions; self-reported readiness and resilience during routine operations may not align with observable performance during an active, large-scale disaster. The demographic instrument did not separately record professional discipline; therefore, a profession-level breakdown of the sample is not available.

Conclusions

In conclusion, this study emphasizes that while disaster readiness among Tehran EMS providers was relatively satisfactory, resilience remained low, highlighting a significant gap between operational readiness and psychological adaptability. The findings show that disaster readiness, work experience, education level, and prior participation in disaster-management workshops are strong predictors of resilience. These results suggest that resilience is not just an individual trait but a capacity developed through professional competence, experience, and structured training. By identifying key factors that influence resilience, this study can deepen the understanding of how disaster readiness can be improved through educational, managerial, and organizational strategies within pre-hospital emergency systems.

Recommendations

Based on the findings, several recommendations are suggested. First, simulation-based disaster education programs should be institutionalized across EMS units to close the gap between theoretical knowledge and real-world response ability. Incorporating resilience-building modules, including stress management, peer support, and reflective practice, into continuing professional education could boost both preparedness and emotional resilience. Policymakers should consider creating standardized national frameworks for disaster-readiness training and regular competency assessments to maintain performance levels. Additionally, fostering interdisciplinary collaboration among EMS, hospitals, and crisis management authorities may enhance coordination and mutual preparedness. Future research should use mixed-methods or longitudinal approaches to examine the evolving relationships among readiness, resilience, and performance outcomes following real incidents.

Implications for practice, education, and research

The implications of this study span clinical practice, education, and research. In clinical practice, the results highlight the importance of integrating psychosocial resilience strategies into EMS protocols to reduce stress-related burnout and ensure service continuity during crises. In education, EMS and nursing curricula should explicitly incorporate disaster management and resilience training through simulation, scenario-based exercises, and reflective debriefing. For research, the findings underscore the need to develop context-sensitive resilience frameworks tailored to the Iranian pre-hospital environment and to assess their impact on both individual and system-level outcomes. Overall, these measures can enhance the preparedness and adaptability of the EMS workforce, ultimately boosting public health response capacity during recurrent natural and human-made disasters.

Abbreviations

CFA

Confirmatory Factor Analysis

CFI

Comparative Fit Index

CHERRIES

Checklist for Reporting Results of Internet E-Surveys

CMIN/DF

Chi-square divided by degrees of freedom

CI

Confidence Interval

EFA

Exploratory Factor Analysis

EMS

Emergency Medical Service(s)

EMT

Emergency Medical Technician

F-DNREI

Farsi/Persian Version of the Disaster Nursing Readiness Evaluation Index

ICC

Intraclass Correlation Coefficient

RMSEA

Root Mean Square Error of Approximation

SD

Standard Deviation

SE

Standard Error

STROBE

Strengthening the Reporting of Observational Studies in Epidemiology

S-CVI

Scale Content Validity Index

S-CVR

Scale Content Validity Ratio

UNISDR

United Nations Office for Disaster Risk Reduction

VIF

Variance Inflation Factor

Authors’ contributions

Sogand Sarmadi: Conceptualization, Formal analysis, Methodology, Data Curation, Writing - Original Draft Visualization; Akbar Zare-Kaseb: Conceptualization, Methodology, Formal analysis, Resources, Data Curation, Writing - Original Draft; Neda Sanaie: Formal analysis, Methodology, Data Curation, Visualization, Review & Editing; Fariba Borhani: Formal analysis, Methodology, Data Curation, Visualization, Review & Editing.

Funding

No external funding.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to the need to ensure participant confidentiality and comply with the country’s laws. Still, they are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Shahid Beheshti University of Medical Sciences, Tehran, Iran (Ethics Code: IR.SBMU.RETECH.REC.1404.345), and was conducted in accordance with the Declaration of Helsinki. Electronic informed consent was obtained from all participants via the online survey platform prior to enrolment; participation was voluntary, anonymous, and revocable at any time without penalty. No personal identifiers were collected, and data were stored on password-protected institutional servers with access restricted to the research team.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.