Exploring the Relationship Between Smartphone Addiction, Attention Difficulties, and Occupational Accident Risk Among Health Science Interns: A Cross-Sectional Study

Abstract

Background:

The widespread use of smartphones among healthcare students raises concerns about cognitive and occupational safety impacts. This study aimed to examine the relationships between smartphone addiction, attention difficulties, and susceptibility to occupational accidents among university students in health science internships.

Methods:

A cross-sectional study was conducted in 2023 with 553 volunteer university students enrolled in the health sciences programs at Yozgat Bozok University Hospital. Participants were selected using a convenience sampling method. The study site was a university hospital in central Türkiye, where students were completing clinical internships. Data were collected using validated scales, and the analysis plan included descriptive statistics, Pearson correlation, and multivariate linear regression to examine associations between variables.

Results:

Significant positive correlations were found between smartphone addiction and attention difficulties (r = 0.510), accident susceptibility (r = 0.504), and daily smartphone use (r = 0.314). Regression analysis showed that attention difficulties were primarily influenced by smartphone addiction. Susceptibility to accidents was mainly predicted by attention difficulties, smartphone addiction, and being a student in nursing, midwifery, or medicine (Adj. R² =0.473). Occupational accident risk was associated with susceptibility to accidents, higher risk perception, off-campus work, lack of occupational health and safety training, attention difficulties, nursing education, and previous accident experience (Adj. R² =0.238).

Conclusion:

The study found that smartphone addiction is significantly associated with increased attention difficulties, which in turn elevate students’ susceptibility to occupational accidents. Smartphone use may thus indirectly increase accident risk in clinical settings.

INTRODUCTION

Hospitals and healthcare institutions are of particular importance when it comes to the occupational health and safety (OHS) of their employees. Preventing workplace accidents and ensuring the safety of healthcare workers are paramount in these settings. Work-related accidents in hospitals and healthcare institutions can occur for various reasons, affecting not only healthcare staff but also patients and visitors. Common causes of accidents include injuries during patient handling or transport, infections, medication administration errors, cuts and puncture wounds from sharp objects, excessive fatigue, exposure to hazardous chemicals, and more.[1]

In occupational safety research, near-miss events—defined as incidents that do not result in harm but with the potential to do so—are increasingly acknowledged as critical precursors to workplace accidents. These events serve as early warning signs, offering valuable opportunities to identify systemic weaknesses, unsafe conditions, and operational lapses before they escalate into actual harm. Saleh et al. (2013)[2] emphasize that near misses represent truncated accident sequences, and when properly identified and analyzed, they enable organizations to interrupt the unfolding of potential accidents through timely interventions. Similarly, Gnoni and Saleh (2017)[3] highlight the role of near-miss management systems as a foundational element of the “observability-in-depth” safety principle. These systems allow organizations to systematically capture and learn from near-miss data, classify them based on violated safety principles, and implement targeted safety improvements. In this sense, near misses are not isolated or inconsequential events, but actionable safety signals that strengthen organizational learning and enhance proactive risk management. By leveraging near-miss data, industries—especially those with high accident potential, such as healthcare—can build more resilient safety cultures and prevent future harm.[4,5]

Occupational accident risk is a multifaceted concept influenced by both internal and external factors. In this study, two dimensions of accident risk were considered: accident proneness (individual susceptibility to accidents) and actual workplace accident experience during internship. Accident proneness reflects an individual’s tendency to be involved in accidents due to cognitive, behavioral, or perceptual factors, and is increasingly recognized as a psychological risk trait relevant in safety-sensitive environments.[6,7] In contrast, actual accident experience during internships provides a tangible measure of safety outcomes in clinical settings. Including both dimensions allows for a more comprehensive assessment of how cognitive factors—such as attention difficulties influenced by smartphone use—translate into both perceived risk and real-world safety events. Recent studies emphasize the need to integrate subjective and objective indicators of occupational safety to better understand accident causality and design effective prevention strategies.[8,9]

Several factors contribute to an employee’s susceptibility to workplace accidents, including personal characteristics, professional experience, job conditions, the working environment, and equipment usage. Individual vulnerability may stem from behaviors or conditions such as inattention, absentmindedness, carelessness, impulsivity, risk-taking tendencies, insufficient training, sleep deprivation, or fatigue. To reduce the risk of workplace accidents, employers must account for these variables, while employees should remain vigilant and consistently follow established safety protocols.[10]

The term addiction refers to the compulsive use of a substance or engagement in a behavior that exceeds its intended purpose. In recent years, new forms of behavioral addiction—such as internet, social media, and smartphone addiction—have gained recognition, with smartphone addiction being considered for inclusion in the DSM-5.[11,12,13,14] Smartphones, as portable and multifunctional devices, are widely adopted across various sectors due to their versatility, ease of use, and convenience.[15] However, excessive smartphone use may lead to addiction, resulting in physical and psychological consequences comparable to those seen in other behavioral addictions. Reported symptoms include impaired vision, musculoskeletal discomfort due to prolonged immobility, headaches, social isolation, and disruptions in daily functioning.[16,17,18]

Smartphone addiction has also been linked to impaired attention, with individuals experiencing difficulties in maintaining concentration and focus.[17] Attention deficiency is typically characterized by an inability to sustain age-appropriate levels of attention and concentration.[19] Addictive behaviors can further aggravate attention-related issues,[20] contributing to symptoms such as forgetfulness, reduced focus, and diminished cognitive awareness.[21,22]

The widespread use of smartphones among healthcare students has raised concerns about their potential impact on cognitive functioning and occupational safety during clinical practice.[19] Studies on workplace accidents have frequently identified attention deficiencies as contributing factors. This study aims to examine the relationship between smartphone addiction, attention deficits, and susceptibility to workplace accidents. Despite the recognized importance of these variables, there is a notable gap in research exploring their interconnections, particularly among health science interns. Therefore, this study offers a meaningful contribution to the literature by addressing this understudied area.

METHODS

Research type

This study has a cross-sectional design.

Research location and time

The research was conducted among students studying in the field of healthcare at Yozgat Bozok University and engaging in professional practice at healthcare institutions. The data were collected in March–April of the year 2023.

Research population and sample

The research was conducted on students participating in healthcare practice at Yozgat Bozok University Research and Application Center. The study included university students enrolled in the Medical School, the Faculty of Health Sciences, and the Vocational School of Health at Yozgat Bozok University. Participants were selected using a convenience sampling method. An a priori power analysis was conducted using G*Power software (version 3.1; Heinrich Heine University Düsseldorf, Düsseldorf, Germany) to determine the minimum sample size required for the study. Assuming a medium effect size of R² = 0.15 for the association between occupational accident risk and predictor variables, a Type I error rate (α) of 0.05, and a desired statistical power of 0.95 (1 – β), the minimum required sample size was calculated to be N = 172 participants. This calculation was based on a multiple linear regression model with 10 predictor variables, which included factors such as attention deficit scores, smartphone addiction scores, age, gender, academic discipline (e.g. medicine, nursing, midwifery), working status outside school, and OHS training. The achieved sample size of 553 participants in this study exceeded the minimum requirement, thereby increasing the statistical reliability and generalizability of the results.

Data collection methods

Before data collection, participants were informed about the purpose and scope of the study, and verbal informed consent was obtained. Data were gathered using a structured questionnaire administered to students actively engaged in professional practice. Prior to survey completion, all participants were provided with clear information regarding the study’s objectives and significance.

To minimize bias, participation was both voluntary and anonymous, reducing the potential for social desirability bias. All eligible health sciences students were invited to participate to avoid selection bias. Standardized and validated instruments were employed to prevent measurement bias. Uniform instructions were given to all participants, and survey items focused on recent experiences to minimize recall bias. Potential confounding variables were addressed using multivariate analysis.

Data collection tools

Sociodemographic Questionnaire: The questionnaire, which comprised 14 questions, was developed based on the literature by the researchers, addressing aspects such as age, gender, marital status, smartphone usage duration, experience of occupational accident, and OHS risk level of the internship site. Students were asked to assess the OHS risk level of the areas where they conducted their clinical practice in the hospital and to rate the risk as one of the following: 0 = No risk, 1 = Slightly risky, 2 = Risky, 4 = Very risky.

Smartphone Addiction Scale-Short Form (SAS-SV)

SAS-SV used in this study was originally developed by Kwon et al.(2013),[17] and its Turkish validity and reliability were established by Noyan et al. (2015).[23] Designed to provide a concise and practical measure of smartphone addiction severity, the scale is primarily used with adolescents and young adults, though it has also been validated for use in adult populations. The SAS-SV consists of 10 items, each rated on a 6-point Likert scale (1 = Strongly disagree to 6 = Strongly agree), with total scores ranging from 10 to 60. Higher scores reflect greater levels of smartphone addiction. Notably, the scale does not include any reverse-coded items. In the present study, the scale demonstrated excellent internal consistency, with a Cronbach’s alpha of 0.90.

Adult Attention Deficit Disorder (AADD) Scale

The Adult Attention Deficit Disorder (AADD) scale is a self-report instrument developed by Kessler et al. (2005)[24] to assess attention-related difficulties in adults. It is widely used to screen for symptoms associated with AADD, particularly in academic, occupational, and daily life contexts. The scale consists of items rated on a 5-point Likert scale (0 = Never to 4 = Very Often), with higher scores indicating greater levels of attention-related symptoms. The Turkish version of the scale, whose validity and reliability were established by Doğan et al. (2009),[25] includes 18 items and is divided into two subscales: attention deficiency and hyperactivity/impulsivity. The AADD scale is considered a reliable and valid tool for use in adult populations and is frequently employed in studies examining cognitive functioning, behavioral tendencies, and risk-related outcomes such as occupational accidents. The total Cronbach’s alpha for the scale was found to be 0.89 in this study,

Occupational Accident Susceptibility Questionnaire (OASQ)

The questionnaire, developed by the researchers based on a review of the relevant literature,[26] consists of 23 items rated on a 5-point Likert scale (0 = Never to 4 = Very often). It includes four subdimensions: psychological predisposition (10 items), physical predisposition (5 items), sociological predisposition (3 items), and work-related predisposition (4 items). Higher scores reflect greater susceptibility to accidents. In this study, the internal consistency of the scale was found to be high, with a Cronbach’s alpha of 0.91.

Occupational Accident Risk Questionnaire (OARQ)

This questionnaire was developed by the researchers based on the literature.[26] The questionnaire consists of 15 items rated on a 5-point Likert scale (0 = Never to 4 = Very often). The items in the scale were developed based on the tasks and procedures performed in the hospital setting, as well as the specific hazards and risks present in that environment. Higher scores on the questionnaire indicate higher susceptibility to accidents. In this study, the internal consistency of the scale was found to be 0.94.

Statistical analysis

Data were analyzed using the SPSS software package (version 25; IBM Corp., Armonk, NY, USA). Scores from the measurement scales were standardized to a 0–100 scale for comparability. Independent samples t-tests and one-way ANOVA were employed to compare mean scores across categorical independent variables. Pearson’s correlation coefficient was used to evaluate relationships among the primary study variables. To assess predictors of occupational accident risk, linear regression analysis was conducted with the occupational accident risk score as the dependent variable. Independent variables included smartphone addiction, attention deficiency, susceptibility to occupational accidents, and relevant sociodemographic characteristics. The backward elimination method was applied to identify significant predictors, and the final model included only variables that remained statistically significant. A P value of less than 0.05 was considered indicative of statistical significance.

Ethics

Ethical approval for the research was obtained from the Bozok University Ethics Committee on June 20, 2023, with decision number 04/07. Informed consent was obtained from the participants. The research was conducted in accordance with the principles of the Helsinki Declaration.

RESULTS

A total of 553 students participated in the study, with 74.4% identifying as female. Table 1 presents the descriptive characteristics of the study participants. When family income status was classified as income less than expenses, income equal to expenses, and income greater than expenses, 66.2% of participants were found to have income equal to expenses. During internships, 53.7% used smartphones for 1 h daily, while 34.2% used them for 3–4 h daily. A small proportion (3.8%) reported experiencing accidents in the workplace, while 67.6% had received OHS training beforehand. Additionally, 59.3% perceived their practice areas as having low or no OHS-related risks [Table 1].

Table 1.

Smartphone Addiction Scale scores’ mean according to student characteristics

Variable	Category	Count	Col.%	SAS Scores		t/F
				Mean	SD	P
Gender	Male	125	22.6	29.9	11.04	−0.853
	Female	428	77.4	30.8	10.13	0.394
Age	18–19	73	13.2	29.1	11.79	0.816
	20	116	21.0	30.8	9.93	0.515
	21	130	23.5	30.3	9.41
	22	104	18.8	30.4	9.75
	≥23	130	23.5	31.7	11.18
	Mean ± SD			21.4	2.22
Educational Institution	Medical School	60	10.8	33.5	9.35	9.279
	Faculty of Health Sciences	305	55.2	31.6	10.27	<0.006
	Vocational School of Health	188	34.0	28.1	10.35
Department of Institution	Medicine	60	10.8	33.5	9.20	7.062
	Nursing	146	26.4	33.5	10.34	<0.000
	Midwifery	136	24.6	30.5	9.77
	Paramedic	72	13.0	29.5	10.75
	Anesthesia-radiology	72	13.0	26.7	9.41
	Other Health Departments	67	12.1	27.3	10.71
Grade	1	166	30.0	28.6	10.75	3.934
	2	204	36.9	31.4	10.26	0.009
	3	77	13.9	30.1	9.91
	≥4	106	19.2	32.6	9.70
Family income	Income less than expenses	94	17.0	32.5	10.56	3.068
	Income equals expense	366	66.2	29.8	9.73	0.047
	Income more than expenses	93	16.8	31.7	12.09
Working situation outside of school	No	471	85.2	30.8	10.05	1.271
	Ye	82	14.8	29.2	11.84	0.204
	Total	553	100.0	30.6	10.34
Smartphone usage time during internship (hours)	1	297	53.7	29.1	10.11	7.153
	2	122	22.1	31.2	10.49	0.000
	3	65	11.8	31.3	9.64
	≥4	69	12.5	35.2	10.35
Daily smartphone usage time (hours)	1–3	123	22.2	25.6	9.64	22.891
	4–5	189	34.2	29.8	9.77	0.000
	6–7	109	19.7	31.5	9.35
	≥8	132	23.9	35.6	10.24
Experience of occupational accident	None	532	96.2	30.6	10.40	0.198
	Yes	21	3.8	30.1	8.83	0.843
Getting OHS training before internship	None	179	32.4	32.9	10.35	3.696
	Yes	374	67.6	29.5	10.17	0.000
Risk level of the application area in terms of OHS	No risk	99	17.9	28.5	10.27	1.689
	Less risky	229	41.4	31.1	10.38	0.168
	Risky	166	30.0	31.0	9.84
	Very risky	59	10.7	30.8	11.46
	Total	553	100.0	30.6	10.34
Smartphone usage time during internship (hours)	1	297	53.7	29.1	10.11	7.153
	2	122	22.1	31.2	10.49	0.000
	3	65	11.8	31.3	9.64
	≥ 4	69	12.5	35.2	10.35
Daily smartphone usage time (hours)	1–3	123	22.2	25.6	9.64	22.891
	4–5	189	34.2	29.8	9.77	0.000
	6–7	109	19.7	31.5	9.35
	≥8	132	23.9	35.6	10.24

SAS=Smartphone Addiction Scale, SD=Standard deviation, OHS=Occupational Health and Safety, t=Independent Student t-test, F=One-way ANOVA test

The average Smartphone Addiction Scale (SAS) score was 30.6 ± 10.34, indicating low addiction levels. Students in medical and nursing programs (33.5) and those in higher academic years (32.6) had higher scores. Smartphone addiction was significantly linked to lower income relative to expenses (32.5) but showed no association with gender, age, or working outside school.

Higher addiction scores were found among students using smartphones for ≥4 h during internships (35.2), daily use ≥8 h (35.6), or those without OHS training (32.9). Smartphone addiction was not significantly associated with workplace accidents or perceived OHS risks in practice areas [Table 1].

The mean Adult Attention Deficit Scale (AADD) score was 39.4 ± 14.3, suggesting low attention deficit levels. Higher scores were observed in medical (45.7) and nursing students (44.6) and in those in advanced academic years (42.9). Attention deficit was significantly associated with smartphone usage for ≥4 h during internships (42.3), daily use ≥8 h (42.6), workplace accidents (46.2), and lack of OHS training (42.0). No significant relationships were found with gender, age, family income, or working outside school [Table 2].

Table 2.

Mean scores of distraction, occupational accident susceptibility, and risk of occupational accident according to the characteristics of students

Variable	Category	Distraction		t/F	Proximity to occupational accident		t/F	Risk of occupational accident		t/F
		Mean	SD	P	Mean	SD	P	Mean	SD	P
		Gender	Male	39.3	16.84	−0.033 0.973	39.1	17.87	−0.850 0.396	20.8	19.20	1.512 0.131
Female	39.4		13.48	40.6	16.64		18.1	16.51
Age	18–19	38.1	15.52	0.589 0.671	35.6	18.58	2.041 0.087	16.8	18.63	0.587 0.672
	20	38.6	13.75		39.5	17.77		19.2	17.65
	21	38.9	12.99		40.6	16.39		17.8	16.91
	22	40.1	15.55		41.1	16.01		18.8	16.94
	≥23	40.6	14.34		42.4	16.12		20.2	16.43
Educational Institution	Medical School	44.1	15.84	11.18 0.001	45.7	16.38	21.79 0.000	20.2	18.31	10.92 0.000
	Faculty of Health Sciences	40.7	13.97		43.0	15.70		21.3	17.28
	Vocational School of Health	35.7	13.53		33.9	17.26		14.1	15.69
Department of Institution	Medicine	44.1	15.84	7.586 0.000	45.7	16.38	12.241 0.000	20.2	18.31	8.512 0.000
	Nursing	42.2	14.05		44.6	14.57		24.1	17.98
	Midwifery	40.3	13.10		42.6	15.31		19.4	16.13
	Paramedic	38.6	13.39		38.5	15.08		19.0	17.05
	Anesthesia-radiology	32.1	12.70		30.0	16.32		10.9	14.45
	Other Health Departments	35.7	14.92		33.8	20.91		12.1	14.48
Grade	1	35.9	15.48	6.443 0.000	35.2	18.29	8.683 0.000	14.4	17.15	5.495 0.001
	2	40.8	12.76		41.1	16.97		19.8	17.02
	3	38.2	12.63		41.8	13.92		20.7	17.21
	≥4	42.9	15.18		45.2	14.65		21.9	16.40
Family income	Income less than expenses	42.2	13.78	2.555 0.079	44.7	15.88	3.964 0.020	18.8	16.60	0.023 0.977
	Income equals expense	38.5	13.80		39.4	16.53		18.6	16.87
	Income more than expenses	39.9	16.34		39.0	18.81		19.0	19.03
Working situation outside of school	No	39.5	14.27	0.412 0.680	40.5	16.67	0.745 0.457	18.1	16.71	2.203 0.028
	Yes	38.8	14.48		39.0	18.35		22.6	19.28
Smartphone usage time during internship (hours)	1	38.1	14.15	2.530 0.056	38.2	17.52	3.373 0.018	16.2	16.47	4.895 0.002
	2	39.4	13.24		41.6	15.42		20.9	16.30
	3	42.0	15.96		44.0	17.18		21.2	19.62
	≥4	42.3	14.60		43.0	15.66		23.2	17.77
Daily smartphone usage time (hours)	1–3	35.5	13.98	7.591 0.000	36.8	17.72	4.511 0.004	17.6	17.16	3.371 0.018
	4–5	37.9	12.69		38.8	16.88		16.2	15.59
	6–7	42.3	15.84		43.6	14.63		20.7	16.87
	≥8	42.6	14.36		42.6	17.31		21.7	19.04
Experience of occupational accident	None	39.1	14.08	−2,251 0.025	40.1	16.93	−1,168 0.243	18.4	17.11	−2,432 0.015
	Yes	46.2	18.01		44.5	16.55		27.6	16.75
Getting OHS training before internship	None	42.0	13.57	3.015 0.003	43.2	16.41	2,848 0.005	22.6	18.85	3.680 0.000
	Yes	38.1	14.47		38.8	17.00		16.9	16.01
Risk level of the application area in terms of OHS	No risk	36.1	16.40	2,401 ,067	34.2	20.73	5.462 0.001	10.7	15.21	10.840 0.000
	Less risky	40.2	14.06		41.3	15.18		18.8	16.75
	Risky	39.5	13.44		41.4	15.63		22.1	16.99
	Very risky	41.3	13.17		43.1	17.87		22.3	18.30
	Total	39.4	14.29		40.2	16.92		18.7	17.17

OHS=Occupational Health and Safety, t=Independent Student t-test, F=One-way ANOVA test

Occupational accident susceptibility

Students studying medicine (46.1) and nursing (44.6), those in higher academic years (45.2), and those with lower incomes (44.7) had significantly higher susceptibility scores. Occupational accident risk was also higher for students in medical (20.9) and nursing programs (24.1), those in advanced years (21.9), and those working outside school (22.6). No associations were found with gender, age, or family income [Table 2].

Significant correlations

Significant correlations included smartphone addiction and attention deficit (r = 0.510), occupational accident susceptibility (r = 0.504), and daily smartphone usage (r = 0.314). A negative correlation was observed between smartphone addiction and OHS training (r = −0.156). Attention deficit correlated with occupational accident susceptibility (r = 0.642) and risk (r = 0.343). Positive weak correlations were observed with academic year (r = 0.147) and smartphone usage (r = 0.154). Negative correlations were found with OHS training (r = −0.127). Occupational accident risk correlated positively with perceived practice risk level (r = 0.211), academic year (r = 0.147), and smartphone usage (r = 0.133) but negatively with OHS training (r = −0.155) [Table 3].

Table 3.

Relationship between the characteristics of the students and their distraction, occupational accident susceptibility, and occupational accident risk score with SAS score

Variable	SAS	1	2	3	4	5	6	7	8	9	10
1. Distraction	0.510**	1
2. Occupational accident susceptibility	0.504**	0.642**	1
3. Occupational accident risk	0.272**	0.343**	0.418**	1
4. Experience of occupational accident	−0.008	0.095*	0.050	0.103*	1
5. Risk level of the application area in terms of OHS	0.061	0.082	0.135**	0.211**	0.032	1
6. Getting OHS training before internship	−0.156**	−0.127**	−0.120**	−0.155**	0.016	0.065	1
7. Age	0.033	0.043	0.081	0.033	0.065	0.203**	0.115**	1
8. Grade	0.114**	0.147**	0.194**	0.147**	0.199**	0.304**	0.028	0.465**	1
9. Family income	−0.023	−0.048	−0.098*	0.004	−0.016	−0.072	0.071	0.072	0.049	1
10. Smartphone usage time during internship	0.206**	0.105*	0.129**	0.133**	0.017	0.062	−0.149**	0.008	0.120**	0.111**	1
11. Daily smartphone usage time	0.314**	0.154**	0.094*	0.090*	−0.027	−0.036	−0.080	−0.107*	−0.101*	−0.027	0.264**

**Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed). SAS=Smartphone Addiction Scale, OHS=Occupational Health and Safety. The numbers (1–10) in the column correspond to the numbers of the variables listed in the rows.

Regression analysis

Attention deficit

Smartphone addiction (β = 0.487) was the strongest predictor, explaining 27.5% of the variance (Adj. R² = 0.275).

Occupational accident susceptibility

Factors included attention deficit (β = 0.494), smartphone addiction (β = 0.241), and study field (nursing, midwifery, medicine), explaining 47.3% of the variance (Adj. R² = 0.473).

Occupational accident risk

Key predictors included accident susceptibility (β = 0.307), perceived practice risk (β = 0.157), and working outside school (β = 0.105), explaining 23.8% of the variance (Adj. R² = 0.238). Age, gender, family income, and academic year were not significant in predicting attention deficit or occupational accident risk [Table 4].

Table 4.

Analysis of factors affecting occupational accident risk and susceptibility with linear regression backward model

Occupational accident risk† (Adj. R2=0.238)	Unstandardized Coefficients		Standardized Coefficients	t	P	95.0% Confidence Interval for B
	B	Std. Error	β			Lower Bound	Upper Bound
(Constant)	−6.115	3.351		−1.825	0.069	−12.698	0.468
Nursing department	3.435	1.529	0.088	2.247	0.025	0.432	6.438
Distractibility	0.129	0.059	0.107	2.204	0.028	0.014	0.244
Work accident susceptibility	0.312	0.050	0.307	6.256	0.000	0.214	0.410
Getting OHS training before the internship starts	−3.801	1.432	−0.104	−2.654	0.008	−6.614	−0.988
Experience of occupational accident	6.965	3.354	0.078	2.077	0.038	0.376	13.554
Risk level of the application area in terms of OHS	3.018	0.732	0.157	4.123	0.000	1.580	4.456
Working a job outside of school	5.200	1.800	0.108	2.889	0.004	1.665	8.736
Occupational accident susceptibility†† (Adj. R2=0.473)
(Constant)	6.630	2.864		2.315	0.021	1.004	12.256
Smartphone addiction	0.394	0.062	0.241	6.357	0.000	.272	.516
Distractibility	0.585	0.043	0.494	13.603	0.000	0.501	0.670
Family income level	−1.755	0.929	−0.060	−1.889	0.059	−3.581	0.070
Risk level of the application area in terms of OHS	1.126	0.595	0.059	1.891	0.059	−0.044	2.295
Medicine Department	4.655	1.867	0.086	2.494	0.013	0.988	8.322
Nursing Department	3.893	1.377	0.102	2.826	0.005	1.187	6.598
Midwifery Department	4.106	1.375	0.105	2.986	0.003	1.405	6.806
Distractibility††† (Adj. R2=0.275)
(Constant)	17.721	1.911		9.271	0.000	13.966	21.476
Smartphone addiction	0.673	0.051	0.487	13.250	0.000	0.573	0.773
Anesthesia-radiology Department	−4.642	1.604	−0.109	−2.893	0.004	−7.793	−1.491

^†Independent variables: Smartphone addiction, distraction, work accident susceptibility, age, gender (dummy), class studied, family income level, working in a job outside of school, time of using a smart phone during internship, time of using a smart phone daily, internship-related occupational accident experience (dummy), receiving occupational health and safety training (dummy) before starting the internship, risk level of the practice area in terms of occupational health and safety, department studied (dummy). ^††Independent variables: Smartphone addiction, distraction, age, gender (dummy), department studied (dummy), class studied, family income level, working outside of school (dummy), time of using smart phone during internship, time of using smart phone per day, internship-related occupational accident experience (dummy), getting occupational health and safety training (dummy) before starting the internship, risk level of the application area in terms of occupational health and safety. ^†††Independent variables: Smartphone addiction, age, gender (dummy), department studied (dummy), class studied, family income level, duration of smartphone use during internship, daily smartphone usage time

DISCUSSION

In this study, the relationship between smartphone addiction, attention deficit, and susceptibility to workplace accidents among university students participating in healthcare internships was examined.

The risk of workplace accidents was defined as situations where students face the possibility of encountering accidents as they transition to practical training fields.[5] Several factors were identified as contributing to an increased risk of workplace accidents, including heightened susceptibility to accidents, increased perception of high-risk practice areas in terms of OHS, working in jobs outside of school, higher levels of attention deficit, lack of OHS training before internships, studying nursing, midwifery, or medicine, and experiencing workplace accidents during internships. Together, these factors accounted for 23.8% of the variance in workplace accident risk scores (Adj. R² = 0.238).

A study conducted among healthcare workers identified sharp object injuries, needlestick incidents, and exposure to blood or other bodily fluids as the most frequently reported types of occupational injuries.[27] Aygün (2020)[28] found that paramedics, midwives, nurses, and doctors were more likely to experience workplace accidents, in that order. Additionally, they concluded in the same study that individuals who did not receive OHS training were more exposed to workplace accidents. The higher risk in nursing as a profession might be due to students in this field being more active in the practice area and having limited experience.[28,29,30] The fact that medical students are more often in an observer role and perform fewer invasive procedures during the day could explain this difference.

The role of education in OHS

Education is a critical component in fostering awareness of OHS. It promotes behavioral changes and equips individuals with knowledge to navigate risks effectively. Consistent with prior studies,[31] our findings indicate that students who had not received OHS training faced a higher risk of workplace accidents.

Workplace accidents are influenced by both environmental and personal factors, with cognitive conditions playing a significant role. Carelessness and attention deficits are particularly detrimental. In our study, attention deficits were significantly correlated with both heightened susceptibility to accidents and increased workplace accident risks. Students perceiving practice areas as high-risk demonstrated greater accident susceptibility, possibly due to heightened risk awareness. Conversely, students unaware of workplace hazards may struggle to recognize and assess risks accurately.

Smartphone addiction and attention deficit

A significant relationship was observed between smartphone addiction, attention deficit, workplace accident susceptibility, and risk. Students with higher daily smartphone usage during internships exhibited greater attention deficits. This aligns with prior studies indicating that excessive smartphone use exacerbates attention-related issues.[19,32,33] In Turkey, smartphone usage among children aged 6–15 years is 76.1%, and internet penetration increased from 82.7% in 2021 to 91.3% in 2024.[34] Among university students, smartphone and internet usage rates are even higher compared to children in this age group. Smartphone addiction, a condition with adverse effects on both mental and physical health, is linked to outcomes such as social isolation, anxiety, decreased sleep quality, fatigue, and reduced workplace performance.[33,35,36]

The widespread use of smartphones has been associated with various physical health issues, including shoulder, back, neck, leg, and wrist pain. According to Kim et al.,[37] back pain was found to be positively correlated with the size of the smartphone’s liquid crystal display (LCD), while leg and foot pain showed a negative correlation with the duration of smartphone use.[37] Similarly, a study by Paek reported that excessive smartphone use was positively associated with dry eye syndrome, neck pain, and hand pain.[38]

Several studies have demonstrated a link between smartphone addiction and the occurrence of accidents. In addition, Kwon et al.[39] found that 57.9% of the 441 students in their study experienced accidents or near-miss incidents while using smartphones while walking. Another study by Kim et al.[40] reported that participants classified as smartphone addicts were more likely than non-addicted users to experience accidents (OR = 1.90, 95% CI: 1.26–2.86), falls or slips from height (OR = 2.08, 95% CI: 1.10–3.91), and collisions or crashes (OR = 1.83, 95% CI: 1.16–2.87).

While no significant linear regression was found between smartphone addiction and students’ academic fields in this study, t-test results revealed significantly higher smartphone addiction among medical students. This may stem from reduced caregiving responsibilities, greater flexibility in schedules, or increased opportunities for smartphone use during presentations. Similar studies have reported higher smartphone addiction among nursing students than practicing nurses.[13]

Education level and attention deficit

Our findings also indicate that attention deficits increase with higher academic levels. This could be attributed to the growing complexity of courses, heightened responsibilities, and future anxieties related to assignments, job placement, and examinations. Similarly, Demirelli (2014)[41] found that attention deficits were more pronounced among nurses with longer service durations.

In this study, medical students in their fourth year or beyond displayed a higher risk of workplace accidents. The demands of advanced clinical training, coupled with increased expectations and active participation, likely contribute to this risk.

The importance of OHS training

In Türkiye, university students are required to receive OHS training before starting their professional practice and internships, and they are also provided with health insurance coverage during the internship period. OHS education plays an indispensable role in reducing workplace accidents. According to OHS regulations, mandatory training hours are determined based on workplace risk levels: low risk: 8 h annually; hazardous: 12 h annually; very hazardous: 16 h annually. A total of 67.6% had received OHS training beforehand. Furthermore, 59.3% perceived their practice areas as having low or no OHS-related risks. Comprehensive OHS training and in-service education have been shown to effectively lower workplace accident rates.[31]

Limitations

Conducted at a single university hospital, this study’s findings may not be generalizable to other universities, regions, or healthcare settings.

In the study, attention deficit was assessed using a self-report scale and was not supported by an objective clinical evaluation.

In addition, it did not account for variations in internship environments (e.g. hospital wards, laboratories, or community settings), which may pose different levels of occupational accident risk.

CONCLUSIONS

This study demonstrated that smartphone addiction is significantly associated with increased attention difficulties among health science interns, which in turn elevate their susceptibility to occupational accidents. While smartphone addiction did not directly predict workplace accidents, its indirect effects through cognitive impairment were evident. Attention difficulties emerged as the strongest predictor of accident susceptibility, underscoring the cognitive risks associated with excessive digital device use. Additionally, occupational accident risk was significantly influenced by factors such as perceived workplace hazards, working off-campus, lack of OHS training, and prior accident experience. These findings highlight the need to address both behavioral and environmental contributors to occupational safety in clinical training settings.

Recommendations

To mitigate occupational accident risks and promote safer learning environments for health science students, the following measures are recommended:

1. Strengthen OHS training: Ensure comprehensive and mandatory OHS education is provided prior to internships, tailored to the specific risks of hospital and clinical settings.

2. Monitor and manage digital device use: Establish institutional guidelines for appropriate smartphone use during internships to minimize distractions and protect cognitive focus.

3. Promote awareness campaigns: Implement awareness programs addressing the impacts of smartphone addiction and attention deficits on professional performance and safety.

4. Enhance support for high-risk groups: Pay particular attention to students in fields such as nursing and midwifery, who show higher susceptibility to accidents, through additional mentoring and supervision during practice.

5. Foster a culture of reporting: Encourage the documentation and evaluation of near-miss events to enhance organizational learning and preempt potential hazards.

By adopting these strategies, academic and clinical institutions can improve occupational safety outcomes and contribute to healthier educational environments.

Authorship

MK, GU, YM, and VAT contributed to the conception and design of the study, as well as to data acquisition, analysis, and interpretation of the findings. All authors participated in drafting the manuscript, critically revising it for important intellectual content, and approving the final version to be published.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.