Do cyberchondria,health anxiety,and exposure to phubbing affect attitudes towards violence against healthcare workers?

Abstract

Background

Violence against healthcare workers is a growing occupational and public health concern worldwide. Recent evidence suggests that digital health-related behaviors and psychological vulnerabilities may influence individuals’ attitudes toward violence in healthcare settings.

Objective

This study aimed to examine the effects of cyberchondria, health anxiety, and exposure to phubbing on attitudes toward violence against healthcare workers in Turkey.

Methods

A cross-sectional online survey was conducted among 1018 adults aged 18 and above in Turkey. Measures included the Cyberchondria Severity Scale, Health Anxiety Inventory, General Exposure to Phubbing Scale, and Attitudes Towards Violence Against Healthcare Workers Scale. Structural validity was evaluated using confirmatory factor analysis (CFA) in AMOS, while internal consistency was assessed through Cronbach's α and composite reliability (CR). Hypotheses were tested using linear regression. Direct and indirect effects were further examined through structural equation modeling (SEM) with bootstrapping.

Results

Cyberchondria, health anxiety, and exposure to phubbing were positively associated with attitudes toward violence against healthcare workers. Phubbing influenced violent attitudes directly (β = .155, p < .001; f² ≈ 0.03) and indirectly through health anxiety (β = .388, p < .001; f² ≈ 0.20) and cyberchondria (β = .259, p < .001; f² ≈ 0.08), supporting Hypotheses 1–3, while the structural equation model supported Hypothesis 4.

Conclusion

The results highlight the role of digital behaviors and health-related psychological factors in shaping violent attitudes, emphasizing the need for digital awareness programs, health communication training, and policies aimed at preventing workplace violence.

Keywords

workplace violence healthcare workers hypochondriasis anxiety health information seeking behavior internet cell phone interpersonal relations

Introduction

Violence encompasses verbal, physical, sexual, economic, and psychological acts intended to harm individuals or groups.¹ Violence against healthcare workers includes acts of aggression by patients or their relatives, threatening both staff well-being and the safety of healthcare delivery.² International and national evidence shows that healthcare professionals face a substantially higher risk of workplace violence than other occupational groups, with some reports indicating rates up to 16 times greater.³ Workplace factors such as long working hours, heavy workload, and limited resources increase the risk of violence in healthcare settings.^4–9 Communication problems and unmet patient expectations may further intensify this risk. For example, 85% of members of the American College of Emergency Physicians reported an increase in workplace violence in emergency departments over the past five years.¹⁰ Similarly, reports from Turkey indicate a rising trend, with documented incidents increasing from 249 in 2022 to 457 by the end of 2023.¹¹

Recent evidence suggests that workplace violence against healthcare workers has increased further in the post-pandemic period.^10,11 The COVID-19 pandemic placed unprecedented pressure on healthcare systems and intensified emotional strain for both patients and healthcare staff.¹² During this period, many individuals experienced heightened fear of severe illness or death, particularly when access to timely care was limited. Delays in treatment, uncertainty about outcomes, and concerns over scarce medical resources such as hospital beds or medications may have increased frustration and feelings of helplessness among patients and their relatives.^13,14 These stressors, combined with rising expectations and reduced tolerance in high-demand environments, may have contributed to more frequent conflicts during healthcare encounters.¹⁵

Studies conducted during and after the pandemic have reported an escalation in aggressive behaviors and threats toward healthcare professionals, particularly in settings such as emergency departments.^13,16,17 Overall, recent literature emphasizes that violence in healthcare is a multidimensional occupational health challenge that calls for psychosocial, organizational, and communication-focused prevention efforts.¹⁸

Previous research has mainly examined violence in healthcare in relation to structural conditions, demographic characteristics, and selected psychosocial factors.^19–21 However, the combined role of cognitive processes, health-related anxiety, and communication experiences has received less attention. In particular, how an individual's internal anxieties interact with social experiences to shape attitudes toward violence has received limited attention. Addressing this gap may help clarify the multidimensional nature of violence in healthcare settings.

The present study focuses on cyberchondria, health anxiety, and exposure to phubbing, and examines how these factors may be related to attitudes toward violence against healthcare workers. The study evaluates both their independent effects and their combined influence within a single framework.

From a theoretical perspective, the study suggests that internal anxieties and social interaction experiences may jointly shape violent attitudes, helping to clarify the mechanisms underlying such behaviors. In practice, these findings may support the development of digital health education initiatives, communication training, and interventions aimed at improving health literacy. To clarify these mechanisms, the following theoretical perspectives outline how each psychosocial factor contributes to the development of violent attitudes in healthcare settings.

Intensive exposure to online health information may lead individuals to overestimate disease likelihoods and remain in a persistent state of uncertainty.²² This cognitive burden can trigger feelings of anger and impatience, particularly when healthcare encounters fail to meet personal expectations. Within the framework of the General Aggression Model (GAM), chronic cognitive and emotional arousal is suggested to lower individuals’ aggression thresholds and increase the likelihood of hostile attitudes in social interactions. Cyberchondria may not directly predict violence, but it can lower individuals’ tolerance in healthcare settings and increase negative attitudes toward healthcare workers.

Individuals with high health anxiety often turn to healthcare services for reassurance and emotional comfort, in addition to medical care. From the perspective of Cognitive Stress Theory, perceived failure to meet these expectations may disrupt emotional regulation processes. Experiences of uncertainty, insufficient information, or perceived loss of control can intensify feelings of frustration and anger. Consistent with the Frustration and Aggression framework, such emotional accumulation may manifest as hostile attitudes and the normalization of aggressive responses toward healthcare workers. Accordingly, health anxiety represents a critical psychological vulnerability that shapes attitudes toward violence in healthcare settings.

Perceived inattentiveness and the diversion of attention to digital devices during face-to-face interactions may intensify feelings of devaluation and social exclusion. Social Exclusion Theory suggests that such experiences can evoke anger and hostility, increasing the likelihood of aggressive attitudes. In healthcare contexts, this perception may be reinforced when patients or their relatives interpret healthcare professionals’ behaviors as indifferent or disengaged. Therefore, exposure to phubbing functions as a social and contextual trigger that amplifies negative attitudes and aggressive tendencies toward healthcare workers.

Taken together, this theoretical framework suggests that digital health behaviors, psychological vulnerabilities, and social interaction experiences operate through interrelated mechanisms that shape attitudes toward violence against healthcare workers. Accordingly, the present study tests both the independent and combined effects of these variables through a set of theory driven hypotheses.

Theoretical framework and hypotheses

Theoretical mechanisms

The theoretical framework of this study is designed to encompass various psychosocial and behavioral theories to better understand attitudes toward violence against healthcare workers. For instance, the Biopsychosocial Model posits that individual psychological factors (e.g., anxiety, stress, cognitive distortions) interact with social experiences (e.g., communication deficits, perceived neglect) to shape behavior²³; whereas the GAM emphasizes that the accumulation of personal and environmental stressors can trigger aggressive and violent behaviors.²⁴ Other theories, including the Cognitive Stress Theory, the Frustration and Aggression Hypothesis, and the Social Exclusion Theory, are discussed in relation to specific hypotheses in the following section.

Together, these theories help explain how cyberchondria, health anxiety, and exposure to phubbing may shape individuals’ attitudes toward violence against healthcare workers. Specifically, internal anxieties (cyberchondria and health anxiety) may lead individuals to exaggerate perceived threats and elevate stress levels^25–27 while external perceptions of neglect (exposure to phubbing) can cause individuals to feel devalued and overlooked.²⁸ The combination of these processes lowers tolerance thresholds, diminishes empathy and patience, and may result in aggressive behaviors as part of the stress response. This mechanism forms the causal basis underlying the study's hypotheses.

Hypotheses

Cyberchondria and attitudes toward violence (H1)

Excessive online searching for health related information may amplify perceived threat, uncertainty, and the need for reassurance, particularly during healthcare encounters. When individuals approach healthcare services with heightened expectations for certainty and control, unmet expectations can intensify frustration and impatience. From the perspective of Cognitive Stress Theory, sustained stress arising from perceived health threats can disrupt cognitive appraisal and emotional regulation processes.^14,17 According to the GAM, sustained cognitive and emotional arousal can reduce tolerance and increase hostile attitudes toward others, including healthcare professionals.²⁴ Empirical evidence further suggests that cyberchondria is associated with elevated anxiety and maladaptive behavioral responses in health-related contexts.^29–31 Accordingly, cyberchondria is expected to be positively associated with attitudes toward violence against healthcare workers.

Hypothesis 1 (H1): Cyberchondria has a positive and significant effect on attitudes toward violence against healthcare workers.

Health anxiety and attitudes toward violence (H2)

Health anxiety is characterized by persistent concern about health and heightened vigilance toward bodily sensations, which may intensify perceptions of threat during healthcare encounters.³² When individuals seek reassurance and emotional security from healthcare services, perceived inadequacy of information, uncertainty, or lack of control can amplify frustration and anger. From the perspective of Cognitive Stress Theory, sustained anxiety disrupts emotional regulation and coping processes, increasing irritability and negative interpersonal reactions.^15–21,23 Consistent with the Frustration and Aggression Hypothesis, unmet expectations and perceived goal blockage may channel accumulated distress into hostile or aggressive attitudes.^33,34 Empirical evidence further indicates that higher levels of health anxiety are associated with maladaptive emotional responses and negative attitudes toward others under stress.^35–37 Accordingly, health anxiety is expected to be positively associated with attitudes toward violence against healthcare workers.

Hypothesis 2 (H2): Health anxiety has a positive and significant effect on attitudes toward violence against healthcare workers.

Exposure to phubbing and attitudes toward violence (H3)

Exposure to phubbing may signal inattentiveness and social disregard during face to face interactions, which can undermine individuals’ sense of being valued and acknowledged.³⁸ From the perspective of Social Exclusion Theory, perceived threats to belongingness or experiences of social rejection are likely to evoke negative emotional responses such as anger and frustration.³⁹ In healthcare settings, these reactions may be intensified when patients or their relatives interpret digitally distracted behaviors as indifference or neglect on the part of healthcare professionals. Empirical evidence indicates that phubbing is associated with negative emotional reactions, perceived neglect, and interpersonal conflict, all of which may erode tolerance and patience in social interactions.^40–43 Accordingly, exposure to phubbing is expected to be positively associated with attitudes toward violence against healthcare workers.

Hypothesis 3 (H3): Exposure to phubbing has a positive and significant effect on attitudes toward violence against healthcare workers.

Combined effect of psychosocial variables (H4)

The Biopsychosocial Model and the GAM emphasize that violent attitudes and behaviors do not arise from isolated factors but from the dynamic interaction of psychological vulnerabilities and social contextual stressors.^24,44 In healthcare settings, cyberchondria and health anxiety may increase perceived threat and emotional sensitivity. At the same time, exposure to phubbing can lead individuals to feel neglected or socially excluded. When these processes co-occur, tolerance thresholds may be further reduced, emotional regulation may be impaired, and aggressive tendencies may be reinforced through cumulative and mutually amplifying mechanisms. Examining these variables simultaneously therefore provides a multidimensional psychosocial framework for understanding attitudes toward violence against healthcare workers.

Hypothesis 4 (H4): Cyberchondria, health anxiety, and exposure to phubbing collectively have a significant and positive effect on attitudes toward violence against healthcare workers.

Study objective

The aim of this study is to evaluate the effects of exposure to cyberchondria, health anxiety, and phubbing on attitudes toward violence against healthcare workers among individuals aged 18 and older residing in Turkey. The results are expected to contribute both theoretically, by enhancing the understanding of psychosocial mechanisms, and practically, by informing the development of digital health education and communication strategies.

Method

Population and sample

The study population consisted of individuals aged 18 and older residing in Turkey. Inclusion criteria required participants to be active recipients of healthcare services in Turkey, have prior experience using the internet, and complete the online survey in full. Questionnaires with missing data were excluded from the analysis. A total of 1018 participants, selected through convenience sampling, were included in the study. Using statistical formulas, the minimum sample size required to represent the country's population was calculated as 384 participants, and the study's sample exceeded this number.⁴⁵

Measurement instruments

Data were collected using a Personal Information Form, the Cyberchondria Severity Scale, the Generalized Phubbing Exposure Scale (GSE), the Violence Against Healthcare Workers Scale, and the Health Anxiety Inventory. The number of items, dimensions, scoring ranges, and Cronbach's alpha values for each scale are summarized below:

Personal information form: A 7-item form designed to collect participants’ demographic information, including age, gender, marital status, educational level, occupation, monthly household income, and daily internet usage.

Cyberchondria severity scale: This scale consists of 12 items across four dimensions (extremism, anxiety, reassurance seeking, and strain). Responses are rated on a 5-point Likert scale ranging from 1 (“Never”) to 5 (“Always”), with total scores ranging from 12 to 60. The scale's reliability, as indicated by Cronbach's alpha, has been reported as 0.862.⁴⁶

Health anxiety inventory: This inventory comprises 18 items across two subscales (excessive sensitivity to physical symptoms and anxiety about potential negative consequences of illness). Items are rated on a 4-point Likert scale ranging from 0 to 3, yielding a total score range of 0–54. The inventory's reliability, as indicated by Cronbach's alpha, has been reported as 0.918.⁴⁷

Generalized phubbing exposure scale (GSE): This scale consists of 22 items across three dimensions (perceived norms, feelings of neglect, and interpersonal conflict). Responses are rated on a 7-point Likert scale ranging from 1 (“Never”) to 7 (“Always”), with total scores ranging from 22 to 154. The scale's reliability, as indicated by Cronbach's alpha, has been reported as 0.87.⁴⁸

Violence against healthcare workers scale: This scale comprises 17 items across two dimensions (anger and use of violence). Items are rated on a 5-point Likert scale ranging from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”), with total scores ranging from 17 to 85. The scale's reliability, as indicated by Cronbach's alpha, has been reported as 0.83.²¹

Data collection and analysis

Data were collected via an online survey between October 15, 2023, and February 15, 2024. For each scale, minimum, maximum, mean, and standard deviation values were calculated. Skewness and kurtosis coefficients were also examined, and the data were considered to be normally distributed.^49–52 Reliability and construct validity were assessed using Cronbach's alpha, confirmatory factor analysis (CFA), and structural validity indices (AVE and CR).⁵³ Discriminant validity of the scales was examined using the Fornell–Larcker criterion. Pearson correlation coefficients and internal consistency values were computed to determine relationships among the main variables. Simple linear regression analyses were conducted to examine the effects of cyberchondria, phubbing, and health anxiety on attitudes toward violence against healthcare workers. Structural equation modeling (SEM) was conducted to examine the structural relationships among the main variables. Path significance was evaluated using 95% bootstrap confidence intervals. Within this framework, path analysis was employed to test the overall structural relationships and to evaluate the proposed theoretical model as an integrated system. All analyses were conducted using SPSS 26 and AMOS 24 software.

Results

As the data met normality assumptions,⁴⁹ parametric analyses were conducted. To examine the effects of all scales on each other and on attitudes toward violence against healthcare workers, simple linear regression and SEM analyses were conducted.

Common method bias was evaluated using Harman's single-factor test. The first factor accounted for 24.37% of the total variance. Since this value is below 50%, it was concluded that common method bias was not a concern.⁵⁴ The possibility of multicollinearity was evaluated using the VIF and Tolerance values of the independent variables: Health Anxiety (Tolerance = 0.752, VIF = 1.33), Cyberchondria (Tolerance = 0.756, VIF = 1.323), and GSE (Tolerance = 0.871, VIF = 1.148). These results suggest that multicollinearity was not a concern.⁵⁵ Once the suitability checks were completed, the demographic distribution of the sample was examined.

A total of 1018 participants took part in the study. Of these, 49.8% were aged 18–34, 34.3% were 35–49, 14.2% were 50–64, and 1.7% were 65 or older. Regarding gender, 55.7% of participants were female and 44.3% were male; among them, 39.1% were married and 60.9% were single. In terms of occupation, 21% were civil servants, 14.2% worked in the private sector, 6% were tradespeople, 23.6% were homemakers, 5.4% were retired, 7.8% were self-employed, 15.6% were students, and 6.4% were not employed. With respect to monthly household income, 36.8% reported earning 0–10,000 TL, 34.7% earned 10,001–20,000 TL, 21.9% earned 20,001–30,000 TL, and 6.6% earned 30,001 TL or more. Additionally, 49.4% of participants reported being online for 0–3 h per day, 40.6% for 4–6 h, and 10% for 7 h or more.

Skewness and kurtosis values were used to assess the normality of the data. For data to be considered normally distributed, skewness and kurtosis values should fall within the ±1.5/±2 range.^34–36 All variables across the scales and subdimensions used in this study were within the reference range, indicating that the data were normally distributed. Table 1 presents the descriptive statistics and normality results for all study variables and scale dimensions.

Table 1.

Descriptive statistics and normality test results for study variables.

Scales and dimensions	Min-Max	Mean	Ss.	Skewness	Kurtosis
Cyberchondrıa Severıty Scale	12–58	30.61	8.61	.072	.068
Extremism	3–15	9.08	3.03	−.183	−.578
Anxiety	3–15	8.38	2.72	−.136	−.413
Seeking reassurance	3–15	7.82	2.77	.266	−.317
Strain	3–15	5.33	2.57	.998	.225
Gse Scale	22–145	76.68	25.09	.358	−.327
Perceived norms	9–63	40.84	11.56	−.426	−.501
Feeling of neglect	8–56	21.56	11.25	.730	−.296
Interpersonal conflict	5–35	14.27	7.30	.676	−.384
Health Anxıety Inventory	0–54	17.82	9.39	.756	.952
Hypersensitivity and anxiety to physical symptoms	0–42	14.39	7.68	.685	.680
Negative consequences of the illness	0–12	3.43	2.46	.737	.339
Vıolence Against Healthcare Workers Scale	17–79	42.40	9.72	.586	1.228
Anger	7–35	26.64	5.87	−.865	.906
Use of violence	10–45	15.76	7.25	1.552	1.913

Note. Min–Max = Minimum and Maximum values; SD = Standard Deviation; GSE = General Exposure to Phubbing Scale (GSE).

The results of the CFA indicate that the proposed model demonstrates a strong overall fit with the observed data. An RMSEA value of .046 and a PCLOSE of 1.000 suggest that the model fit is statistically excellent. As summarized in Table 2, the overall measurement model demonstrated acceptable fit indices (RMSEA = .046, CFI = .895), supporting the adequacy of the proposed factor structure. The CMIN/DF value of 3.171, along with key fit indices such as CFI, TLI, IFI, and NFI falling within acceptable ranges, confirms that the factor structures of the model are consistently and robustly represented by the data.^56,57 These results suggest that the measurement model fits the data reasonably well and that the study scales have satisfactory reliability and validity. Figure 1 illustrates the proposed structural model and standardized path coefficients among cyberchondria, health anxiety, phubbing exposure, and attitudes toward violence against healthcare workers.

Figure 1.

Psychosocial mechanisms linking cyberchondria, healthy anxiety and pubbing to violent attitudes toward healthcare workers.

Table 2.

Fit indices for confirmatory factor analysis (CFA).

Fit index	Good fit	Acceptable fit	Default model (Fit)
CMIN/DF	< 2	< 5	3.171 (Acceptable)
NFI	> .95	> .90	.855 (Acceptable)
IFI	> .95	> .90	.896 (Acceptable)
TLI	> .95	> .90	.891 (Acceptable)
CFI	> .95	> .90	.895 (Acceptable)
RMSEA	< .05	< .08	.046 (Good Fit)
PCLOSE	> .05	—	1.00 (Good Fit)

Note: CMIN/DF = Chi-square / degrees of freedom; NFI = Normed Fit Index; IFI = Incremental Fit Index; TLI = Tucker-Lewis Index; CFI = Comparative Fit Index; RMSEA = Root Mean Square Error of Approximation; PCLOSE = Probability of Close Fit.

In addition to the overall measurement model, separate confirmatory factor analyses were conducted for each scale to further verify their individual factor structures.The standardized factor loadings obtained from these analyses are presented in Supplementary Table 3. Standardized factor loadings for all items are presented in the Supplementary Material 2.

As shown in Table 3, the results of the individual confirmatory factor analyses indicate that each measurement instrument demonstrates an acceptable to good model fit. The χ²/df ratios for all scales were below the recommended threshold of 5, while the CFI, TLI, and IFI values exceeded the acceptable criterion of .90. In addition, RMSEA values ranged between .058 and .077, indicating an acceptable approximation of the data to the proposed factor structures. These results provide further support for the construct validity of each scale when evaluated independently. The CFA path diagrams for each scale are presented in the Supplementary Material 1.

Table 3.

Fit indices of individual confirmatory factor analyses.

Scale	χ²/df	CFI	TLI	IFI	RMSEA
Cyberchondria	4.40	0.973	0.962	0.973	0.058
Health Anxiety	4.86	0.935	0.924	0.935	0.062
GSE	4.84	0.937	0.928	0.937	0.076
Violence	4.10	0.941	0.930	0.941	0.077

Note: χ²/df < 5.0, CFI/TLI/IFI > .90, RMSEA < .08 indicate acceptable fit.

Reliability and validity analyses were conducted for all scales used in the study. The internal consistency of the factors was evaluated using Cronbach's α and Composite Reliability (CR) values, with CR values of .947 for the Cyberchondria Severity Scale, .972 for the GSE, .958 for the Violence Against Healthcare Workers Scale, and .930 for the Health Anxiety Inventory. Similarly, Cronbach's α values were calculated as 0.880, .945, .872, and .917, respectively, indicating high internal consistency for all factors.⁵⁸ The reliability of the scales, determined through CR and Cronbach's α values, demonstrates that all scales exhibit an adequate level of reliability (Table 4).⁵⁹

The construct validity of the factors was evaluated using Average Variance Extracted (AVE) and √AVE values. AVE values were found to be. 603 for the Cyberchondria Severity Scale, .618 for the GSE, .580 for the Violence Against Healthcare Workers Scale, and .431 for the Health Anxiety Inventory. AVE values for the Cyberchondria Severity Scale, the GSE and the Violence Against Healthcare Workers Scale exceed .50, meeting the criterion for convergent validity. Although the AVE value for the Health Anxiety Inventory is slightly lower at. 431, the high CR and α values support the reliability of this factor.^60,61

Discriminant validity among the factors was examined using the Fornell–Larcker method. The √AVE value of each factor was greater than its correlations with other factors: √AVE = .776 for the Cyberchondria Severity Scale, .786 for the GSE, .762 for the Violence Against Healthcare Workers Scale, and .656 for the Health Anxiety Inventory. These results indicate that the scales accurately differentiate the constructs they measure, confirming that the factors are validly independent Table 5).⁶² ⁶³

Table 4.

Scale reliability and construct validity results for the main variables.

Factor	CR	AVE	√AVE	Cronbach's α
Cyberchondria	.947	.603	.776	.880
Phubbing	.972	.618	.786	.945
Violence	.958	.580	.762	.872
Health Anxiety	.930	.431	.656	.917

Note:

• Cyberchondria Severity Scale = Cyberchondria; GSE Scale = Phubbing; Health Anxiety Inventory = Health Anxiety; Violence Against Healthcare Workers Scale = Violence.

• CR (Composite Reliability / Bileşik Güvenirlik): Values ≥ 0.70 indicate adequate reliability.

• AVE (Average Variance Extracted): Values ≥ 0.50 indicate good construct validity; values below 0.50 are considered acceptable.

• √AVE (Square Root of AVE): Used to assess discriminant validity; √AVE should be greater than correlations between the respective factors.

• Cronbach's α: Internal consistency coefficient; values ≥ 0.70 indicate good reliability.

Table 5.

Correlations among the main variables and internal consistency coefficients of the scales (AVE, Fornell–Larcker).

Factor	Cyberchondria	Phubbing	Violence	Health anxiety
Cyberchondria	.776	.420	.330	.600
Phubbing	.420	.786	.380	.520
Violence	.330	.380	.762	.440
Health Anxiety	.600	.520	.440	.656

Note:

• The table presents the √AVE (square root of AVE) values along the diagonal and the Pearson correlations between variables in the off-diagonal cells.

• √AVE values are used to assess discriminant validity; each factor's √AVE should be greater than its correlations with other factors.⁶²

In Table 6, simple linear regression analysis was conducted to evaluate the validity of Hypothesis 1 tested in the study. The results indicated that the established model was statistically significant (F = 208.655; p < .001). Additionally, the t-statistic assessing the significance of the regression coefficient was also below the significance threshold (t = 27.409; p < .001).⁶⁴ A positive and significant relationship was observed between the Cyberchondria Severity Scale and attitudes toward violence against healthcare workers (R = 0.413; p < .001). The explained variance was calculated as R² = .170, indicating that 17.0% of the variance in attitudes toward violence against healthcare workers was explained by variability in cyberchondria levels. Overall, cyberchondria showed a significant positive effect on attitudes toward violence against healthcare workers, supporting Hypothesis 1 [p < .001].

Table 6.

The effect of cyberchondria on attitudes and behaviors of using violence against healthcare workers.

Variable	Unstandardized coefficients		Standardized coefficients	t	VIF	F	Model (p)
Variable	B	Std. Error	β	t	VIF	F	Model (p)
Constant	28.130	1.026		27.409		208.65	.000*
Cyberchondria	.466	.032	.413	14.445	1.000

Additional Information / Notes:

• R² = .170, R = .413: Indicates the explanatory power of the model; the independent variable (Cyberchondria Severity) accounts for 17% of the total variance.

• VIF (Variance Inflation Factor): 1.000 → No multicollinearity problem.

• Regression equation: Y = 28.130 + 0.466·X (X = Cyberchondria Severity, Y = Violence Attitude and Behavior).

• p < .001:* Statistically significant.

• Asterisk (*) indicates statistical significance at the p < .001 level.

• The table demonstrates that Cyberchondria Severity significantly affects attitudes and behaviors toward violence against healthcare workers.

In Table 7, simple linear regression analysis was conducted to evaluate the validity of Hypothesis 3 tested in the study. The analysis results indicated that the established model was statistically significant (F = 273.218; p < .001). The t-statistic assessing the significance of the regression coefficient was also below the significance threshold (t = 32.472; p < .001). A positive and significant relationship was observed between the GSE and attitudes toward violence against healthcare workers (R = 0.460; p < .001). The proportion of variance explained by the regression model was calculated as R² = .212, indicating that 21.2% of the variance in attitudes toward violence against healthcare workers was explained by changes in levels of generalized phubbing exposure. Generalized exposure to phubbing (GSE) also had a significant positive effect on attitudes toward violence against healthcare workers, supporting Hypothesis 3 [p < .001].

Table 7.

The effect of GSE on attitudes and behaviors towards violence against healthcare workers.

Variable	Unstandardized coefficients		Standardized coefficients	t	VIF	F	Model (p)
Variable	B	Std. Error	β	t	VIF	F	Model (p)
Constant	28.718	.871		32.972		273.218	.000*
GSE	.178	.011	.460	16.529	1.000	273.218	.000*

Note: See the notes for Table 5 for abbreviations and the meaning of the asterisk (*). R², β, t, and p values provided in each table are specific to that table.

In Table 8, simple linear regression analysis was conducted to evaluate the validity of Hypothesis 2 tested in the study. The analysis results indicated that the established model was significant (F = 248.428; p < .001). The t-statistic assessing the significance of the regression coefficient (t = 58.328; p < .001) also supported this result. A positive and statistically significant relationship was observed between the Health Anxiety Inventory and attitudes toward violence against healthcare workers (R = .443). The explained variance was calculated as R² = .196, indicating that approximately 19.6% of the variance in attitudes toward violence against healthcare workers was explained by changes in health anxiety levels. Health anxiety significantly predicted attitudes toward violence against healthcare workers, supporting Hypothesis 2 [p < .001].

Table 8.

Effect of the Health Anxiety Inventory on attitudes and behaviors toward violence against healthcare workers.

Variable	Unstandardized coefficients		Standardized coefficients	t	VIF	F	Model (p)
Variable	B	Std. Error	β	t	VIF	F	Model (p)
Constant	34.220	.587		58.328		248.42	.000*
Health Anxiety	.459	.029	.443	15.762	1.000	248.42	.000*

Note: For abbreviations and the meaning of the asterisk (*), see the notes for Table 5. The R², β, t, and p values presented in each table are specific to that table.

To assess the overall structural relationships among the study variables, a path analysis was conducted within the SEM framework. This approach enabled the simultaneous examination of the direct and indirect effects of exposure to phubbing on attitudes toward violence against healthcare workers, both directly and indirectly through health anxiety and cyberchondria, thereby testing the proposed model as an integrated structure.

Figure 2 presents the structural equation model of the study variables, illustrating the significant direct and indirect pathways linking exposure to phubbing with attitudes toward violence against healthcare workers through health anxiety and cyberchondria.

Figure 2.

Structural equation model of the study variables. Note: Only the main variables and standardized β values are presented. Fit indices are provided in Table 2.

Table 9 presents the standardized direct paths of the structural equation model. The results indicate that phubbing has significant and positive effects on both the Health Anxiety Inventory and the Cyberchondria Severity Scale (phubbing → Health Anxiety Inventory: β = .155, p < .001; phubbing → Cyberchondria Severity Scale: β = .121, p < .001). Phubbing also has a direct significant effect on the Violence Against Healthcare Workers Scale (β = .155, p < .001; f² ≈ 0.03, small effect).

Table 9.

Structural equation model: paths and significance among main variables.

Independent variable	Dependent variable	Standardized β	SE	C.R.	P	F²
Phubbing	Anxiety	.155	.017	9.299	***	—
Phubbing	Cyberchondria	.121	.016	7.740	***	—
Phubbing	Violence	.155	.019	8.264	***	0.03 (small)
Anxiety	Violence	.388	.053	7.264	***	0.20 (medium)
Cyberchondria	Violence	.259	.044	5.942	***	0.08 (small–medium)
Violence	Angry	.628	.083	7.589	***	—
Violence	Use of Violence	1.000	—	—	—	—

Notes:

• Phubbing = General Exposure to Phubbing Scale (GSE), Cyberchondria = Cyberchondria Severity Scale, Anxiety = Health Anxiety Inventory, Violence = Violence Against Healthcare Workers Scale, Angry = Subdimension of Violence: Anger, Use of Violence = Subdimension of Violence: Use of Violence.

• β (Standardized Coefficient): Indicates the standardized effect of the independent variable on the dependent variable.

• SE (Standard Error): Standard error of the coefficient.

• C.R. (Critical Ratio): Tests the statistical significance of the coefficient; C.R. > 1.96 indicates p < .05 significance.

• p values and asterisks:

• p < .001 indicates significance.

• The table shows that all paths in the SEM model are statistically significant.

• The path “Violence → Use of Violence” is fixed as a reference (β = 1.000); therefore, SE, C.R., and p are not reported.

• For the dependent variable “Violence,” R² ≈ 0.24.

• f² indicates the effect size of each independent variable on “Violence.”

In addition, the Health Anxiety Inventory and the Cyberchondria Severity Scale show positive and significant effects on the Violence Against Healthcare Workers Scale (Health Anxiety Inventory → Violence Against Healthcare Workers Scale: β = .388, p < .001; f² ≈ 0.20, medium effect; Cyberchondria Severity Scale → Violence Against Healthcare Workers Scale: β = .259, p < .001; f² ≈ 0.08, small–medium effect). Attitudes toward violence significantly explain their subdimensions, “angry” and “use of violence” (angry: β = .628, p < .001; use of violence: constant = 1.0).

Furthermore, part of the effect of GSE on the Violence Against Healthcare Workers Scale is mediated indirectly through the Health Anxiety Inventory and the Cyberchondria Severity Scale (GSE → Health Anxiety Inventory → Violence Against Healthcare Workers Scale ≈ .060; GSE → Cyberchondria Severity Scale → Violence Against Healthcare Workers Scale ≈ .031). In terms of explained variance, the model accounts for approximately 24% of the variance in attitudes toward violence (R² ≈ 0.24). Health anxiety showed a moderate effect on attitudes toward violence, while phubbing and cyberchondria had smaller effects. Together, these findings indicate that both digital behaviors and psychological factors play a role in shaping violent attitudes. To further address the structural relationships among the study variables, path analysis within the SEM framework was conducted using a bootstrap approach. The direct and indirect effects of cyberchondria, health anxiety, and exposure to phubbing on attitudes toward violence against healthcare workers are presented in Tables 9 and 10.

Table 10.

Bootstrap results for direct and indirect effects.

Independent variable	Dependent variable	Std. β	Lower (95% CI)	Upper (95% CI)	P
Phubbing	Anxiety	.155	.118	.199	.001*
Phubbing	Cyberchondria	.121	.081	.168	.001*
Phubbing	Violence	.155	.117	.202	.001*
Anxiety	Violence	.388	.277	.526	.001*
Cyberchondria	Violence	.259	.170	.360	.001*
Violence	Angry	.628	.463	.838	.001*
Violence	Use of Violence	1.000	1.000	1.000	—
Phubbing	Perceived Norms	.383	.319	.444	.002*
Phubbing	Feeling Neglected	.923	.843	1.008	.001*
Phubbing	Interpersonal Conflict	1.000	1.000	1.000	—
Cyberchondria	Extremism	1.423	1.187	1.728	.002*
Violence	Use of Violence	1.000	1.000	1.000	—
Phubbing	Perceived Norms	.383	.319	.444	.002*
Cyberchondria	Anxiety	1.828	1.566	2.180	.001*
Cyberchondria	Seeking Reassurance	1.545	1.310	1.819	.002*
Cyberchondria	Strain	1.000	1.000	1.000	—
Anxiety	Hypersensitivity	.877	.721	1.076	.001*
Anxiety	Negative consequences	1.000	1.000	1.000	—

Notes:

• Cyberchondria = Cyberchondria Severity Scale; Anxiety = Health Anxiety Inventory; Phubbing = General Exposure to Phubbing Scale; Violence = Violence Against Healthcare Workers Scale.

• Std. β = Standardized coefficient.

• 95% CI = 95% confidence interval computed via bootstrap.

• p = significance level; p < .001 indicates statistical significance (indicates p < .001).

• “1.000” and “—” indicate reference paths; SE, C.R., and p are not shown.

• Table presents significance and confidence intervals of SEM paths based on bootstrap analysis.

To test Hypothesis 4, which predicts that exposure to cyberchondria, health anxiety, and phubbing together has a significant effect on attitudes toward violence against healthcare workers, a structural equation model (SEM) was applied. In this context, the effects of phubbing, the Health Anxiety Inventory, and the Cyberchondria Severity Scale on the Violence Against Healthcare Workers Scale and its subdimensions were evaluated using the bootstrap method (n = 1500, bias-corrected, 95% confidence interval). The analyses indicated that all structural paths were statistically significant; the lower and upper bounds did not include zero, confirming that the effects of the independent variables on attitudes toward violence and its subdimensions were reliably significant. Table 10 presents the direct and indirect effects of each independent variable on attitudes toward violence, along with standardized β coefficients and statistical significance values.⁶⁵ The results show that phubbing increases attitudes toward violence both directly and indirectly through the Health Anxiety Inventory and the Cyberchondria Severity Scale. In particular, health anxiety emerges as the independent variable with the strongest direct effect (β = .388), while cyberchondria exhibits a moderate direct effect (β = .259). These results indicate that the independent variables significantly predict attitudes toward violence against healthcare workers through both direct and indirect pathways, thereby supporting Hypothesis 4 [p < .001].

Discussion

The results suggest that cyberchondria, health anxiety, and exposure to phubbing are associated with stronger attitudes supporting violence against healthcare workers. Simple regression analyses revealed that cyberchondria explained 17% of the variance, health anxiety 19.6%, and phubbing 21.2% of the variance in attitudes toward violence. SEM results demonstrated that phubbing increased attitudes toward violence both directly and indirectly through health anxiety and cyberchondria, with health anxiety emerging as the strongest direct predictor. In addition, the reliability and validity analyses supported the adequacy of the measurement instruments used in the study.

Cyberchondria and health anxiety appear to influence individuals’ cognitive and emotional processes, thereby increasing negative attitudes and tendencies toward violence.⁶⁶ Our results indicate that individuals with higher levels of cyberchondria exhibit stronger tendencies toward violence against healthcare workers, consistent with prior research linking cyberchondria to anxiety, depressive mood, obsessive thinking, and aggression.^37,67–69 Conversely, some studies have reported weaker or non-significant effects of cyberchondria. These mixed results may reflect differences in digital health literacy, online health-searching habits, and sample characteristics.^57,58

Participants exposed to phubbing exhibited higher tendencies toward violence when they perceived healthcare workers as inattentive or indifferent. Previous studies have shown that phubbing and digital neglect negatively affect social bonds and relationship quality.^70–74 However, some research suggests that the impact of digital communication deficits on violent tendencies may be more limited.⁷⁵ These discrepancies can be attributed to factors such as differences in technology use intensity, cultural norms, or age group characteristics.

Individual risk factors alone are insufficient to explain violent tendencies, while social and institutional conditions appear to amplify such behaviors. Previous research has linked the increasing rates of violence in the healthcare sector to unmet patient expectations, poor communication, long waiting times, and organizational factors.^4,18,67,68 However, some studies have found that individual factors play a more prominent role.⁵ These differences may reflect variations in psychological resilience, coping strategies, and institutional support, underscoring the multidimensional nature of violent attitudes.

Theoretical contributions and implications

The results demonstrate that cyberchondria, health anxiety, and exposure to phubbing jointly shape attitudes toward violence against healthcare workers. These relationships can be interpreted within established theoretical frameworks, particularly the GAM, Cognitive Stress Theory, and the Biopsychosocial Model.

From the perspective of the GAM, heightened cognitive and emotional arousal associated with excessive online health information seeking and health anxiety may lower tolerance thresholds and facilitate hostile attitudes toward healthcare workers. Cognitive Stress Theory further suggests that sustained perceptions of health threat and limited coping resources can intensify frustration and anger during healthcare encounters.

In addition, exposure to phubbing can be interpreted through Social Exclusion Theory, as perceived neglect or interpersonal disregard may trigger feelings of anger and tension. Consistent with the Frustration and Aggression framework, such accumulated distress may increase the normalization of aggressive attitudes in stressful healthcare interactions.

Taken together, these findings support the Biopsychosocial Model by showing that violent attitudes may emerge from the interaction of psychological vulnerabilities and social-contextual stressors. By empirically testing these mechanisms through SEM, the study contributes to theory by clarifying how digital behaviors, internal anxieties, and socially mediated experiences jointly shape attitudes toward violence against healthcare workers.

Practical and policy implications

Beyond documenting the associations between cyberchondria, health anxiety, sociotelism, and violent attitudes, the findings of this study provide several actionable implications for violence prevention in healthcare settings. The results suggest that preventive strategies should not be limited to organizational or security-based measures, but should also address patients’ digital behaviors, psychological vulnerabilities, and interactional experiences.

At the individual and societal level, enhancing digital health literacy emerges as a critical priority. Improving individuals’ ability to critically evaluate online health information may reduce exaggerated threat perceptions and anxiety-driven frustration. Collaborative initiatives involving the Ministry of Health, universities, and media organizations could support the development of verified digital platforms and public education campaigns that promote reliable health information and responsible online health-seeking behaviors.

At the institutional level, the indirect effect of phubbing highlights the importance of managing digitally mediated interactions in healthcare environments. Healthcare institutions may benefit from establishing clear guidelines regarding non-essential device use during patient encounters and from promoting professional self regulation. Case-based training programs and communication-focused interventions may further help healthcare staff recognize the interpersonal consequences of perceived digital neglect and reduce patient frustration.

From a policy perspective, strengthening violence reporting and monitoring mechanisms is essential. User friendly and anonymous reporting systems can encourage disclosure, support data-driven policy development, and facilitate the implementation of deterrent sanctions. Regular training programs for healthcare managers and staff may further enhance awareness, improve communication practices, and contribute to safer healthcare environments.

Limitations and future research

This study has several limitations that should be acknowledged:

The sample was limited to literate adults aged 18 and above residing in Turkey; therefore, the generalizability of the results is restricted.

The use of self-report measures introduces the potential for response bias and social desirability effects.

Due to the cross-sectional design, causal relationships and changes over time could not be examined.

The online survey method excluded individuals without internet access or those unwilling to participate, which may have led to sampling bias.

Finally, the measurement instruments may carry cultural and contextual limitations, which could influence how constructs such as cyberchondria, health anxiety, and phubbing are perceived and reported.

Although the AGFI value was slightly below the conventional .90 threshold, model fit indices should be interpreted together rather than relying on a single cutoff value.⁷⁶ Given that other key fit indices (e.g., RMSEA = .046 and CMIN/DF = 3.17) indicated an overall acceptable model fit, the measurement model was considered adequate for the purposes of this study.

Future research directions include:

Conducting comparative studies in different cultural contexts.

Using qualitative or mixed-method approaches, in addition to quantitative methods, to examine individuals’ perceptions and experiences in depth.

Testing the effects of digital behaviors on violent attitudes toward healthcare workers through experimental or longitudinal studies.

Conclusion

This study examined the effects of exposure to cyberchondria, health anxiety, and sociotelism on violent attitudes toward healthcare workers. The results indicate that these independent variables significantly influence violent attitudes both directly and indirectly. Simple linear regression analyses revealed that cyberchondria, general exposure to phubbing, and health anxiety all have positive and significant effects on violent attitudes toward healthcare workers. SEM showed that phubbing affects violent attitudes both directly and indirectly through health anxiety and cyberchondria.

These results provide guidance for developing strategies to prevent violence against healthcare workers. In particular, awareness programs and educational interventions aimed at reducing cyberchondria, health anxiety, and maladaptive digital behaviors can be effective in mitigating violent attitudes. Health policies and communication strategies should be designed to address violence at both individual and societal levels. Overall, this study offers an important framework for understanding how digital and psychological factors shape violent attitudes toward healthcare workers and contributes contemporary insights to the literature.

Supplemental Material

sj-docx-1-wor-10.1177_10519815261440428 - Supplemental material for Do cyberchondria, health anxiety, and exposure to phubbing affect attitudes towards violence against healthcare workers?

Supplemental material, sj-docx-1-wor-10.1177_10519815261440428 for Do cyberchondria, health anxiety, and exposure to phubbing affect attitudes towards violence against healthcare workers? by Fatma Nuray Kuşcu Şahin and Mehmet Yorulmaz in WORK

Supplemental Material

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Supplemental material, sj-docx-2-wor-10.1177_10519815261440428 for Do cyberchondria, health anxiety, and exposure to phubbing affect attitudes towards violence against healthcare workers? by Fatma Nuray Kuşcu Şahin and Mehmet Yorulmaz in WORK

Footnotes

Acknowledgements

The authors would like to thank all participants for their valuable contributions to this study, which was derived from the doctoral thesis of the corresponding author.

ORCID iDs

Fatma Nuray Kuşcu Şahin

Mehmet Yorulmaz

Ethical approval

The ethics committee permission required for the conduct of the research was obtained with decision 2023/581 taken at the meeting numbered 05 and dated 31.05.2023 of the Selçuk University Non-Interventional Clinical Research Ethics Committee. The study was conducted in accordance with the principles of the Declaration of Helsinki. In addition, permissions for the use of the data collection tools employed in the research were received from the responsible authors via e-mail.

Informed consent

All participants provided informed consent prior to their participation in the study.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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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