Introduction

As of 2023, South Korea recorded an age-standardized suicide rate of 24.8 per 100,000 individuals, the highest among member countries of the Organization for Economic Cooperation and Development (OECD average: 10.7) [1]. Although suicidal ideation is relatively common, not all individuals with suicidal thoughts progress to attempt suicide. A systematic review estimated that between 2.6% to 37% of individuals with suicidal ideation attempt suicide [2]; however, the majority do not act on these thoughts [3]. Therefore, understanding the factors that influence the transition from suicidal ideation to suicide attempt is critical for effective suicide prevention.

A meta-analysis of 23 studies investigating the relationship between rumination and suicidal behavior observed that rumination was significantly associated with both suicidal ideation (Hedge’s g=0.74) and suicide attempts (Hedge’s g=0.26) [4]. Specifically, rumination about the causes, consequences, and symptoms of negative affect has been linked to increased severity of suicidal ideation [5] and elevated risk of suicide attempts [6]. However, prior studies have predominantly focused on general forms of affective rumination rather than thoughts specifically centered on suicide. Given that rumination is characterized by persistent, repetitive, and uncontrollable thoughts, suicide-specific rumination may play a crucial role in this transition.

Recent findings suggest that the specific characteristics of suicidal ideation, including its frequency, severity, persistence, and uncontrollability, play a crucial role in the transition from ideation to attempt [7-10]. For instance, a study involving 506 adolescents aged 12∼21 years found that individuals with more frequent, severe, and persistent suicidal ideation were significantly more likely to attempt suicide 4∼6 years later, even after adjusting for psychiatric diagnoses and prior suicide attempts [7]. Similarly, a large-scale study of 3,916 individuals with a history of suicidal ideation reported that mental disorders such as major depressive episodes increased the risk of suicide attempts by 1.3∼2.1 times. Furthermore, longer durations of suicidal ideation and difficulty controlling suicidal thoughts were associated with a 2.4∼3.6 times higher risk of suicide attempts [8]. More recently, the perceived controllability of suicidal thinking has been linked to elevated short-term suicide risk [10]. Therefore, rumination, which specifically focuses on suicidal ideation, may serve as a key predictor of suicide attempt. Despite its theoretical and empirical relevance, most studies have relied on general rumination measures, such as the Response Styles Questionnaire [11], which assess rumination on negative emotions rather than suicidal thoughts (e.g., “think ‘Why do I always react this way?’” and “analyze your personality to try to understand why you are depressed”) [12]. These measures fail to capture suicide-specific rumination, underscoring the need for a reliable and valid tool specifically designed to assess this construct.

To address this gap, Rogers et al. [13] developed the Suicide Rumination Scale [SRS], an eight-item measure designed to assess the presence, frequency, and intensity of rumination concerning suicidal ideation, intentions, and planning. The SRS has been validated in individuals with a history of suicidal ideation, and the Farsi version has also been validated in Iran [14]. However, despite South Korea’s high suicide rate and the central role of suicide-specific rumination in suicidal ideation and behavior, no validated Korean-language measure of this construct is available, limiting both the research and clinical assessment of Korean-speaking populations.

This study aimed to validate the Korean version of the Suicide Rumination Scale [K-SRS] in a Korean-speaking population. The factor structure, reliability, validity, and screening performance of the scale were examined. We hypothesized that K-SRS scores would be positively correlated maladaptive forms of rumination (e.g., brooding and depressive rumination) but not with deliberate rumination, supporting convergent and discriminant validity. Additionally, we expected positive associations with suicide risk and related cognitive-affective factors (e.g., perceived burdensomeness, thwarted belongingness, attentional control, and attentional bias), supporting concurrent validity. We also examined incremental validity beyond established risk factors, known- group validity based on levels of depression and anxiety, and the ability of the scale to distinguish between individuals with and without a history of suicide attempts.

Materials and Methods

1. Participants and procedures

The participants were recruited from July 2024 to March 2025 via online and offline bulletin board posts at several universities and various websites. The eligibility criteria included: adults aged 18 years or older, computer literacy, and a lifetime history of suicidal ideation. A total of 124 participants (27 males and 97 females) participated in the study, with a mean age of 25.2 years (SD=6.7). Most had completed high school (n=69; 55.6%), were either currently attending school or unemployed (n=91; 73.4%), and were single (n=113; 91.1%; Table 1).

All the study procedures were conducted online. Upon providing informed consent, the participants received a link to the survey and behavioral task. The survey was administered using Google Forms, whereas the behavioral task was conducted using Pavlovia. This study was approved by the Institutional Review Board [IRB] of Pusan National University (PNU IRB/2024_54_HR). All the participants provided written informed consent.

2. Measures

1) Suicide Rumination Scale [SRS]

The SRS is a self-report measure of suicide-specific rumination. The participants first answer a screening question about lifetime suicidal ideation, and those who respond affirmatively complete an eight-item scale. Items are rated on a five-point Likert scale (0=“almost never” to 4=“almost always”), assessing the frequency of perseverative suicidal thoughts and images. The scale has demonstrated high internal consistency (McDonald’s ω=.91) [13]. The Korean version was developed through a forward-back translation process with permission from the original author (M. L. Rogers). The two translators produced separate versions that were merged into one draft and back-translated into English. The back-translation was reviewed by the original author to ensure accuracy and conceptual equivalence, after which the Korean version was finalized.

2) Korean-Ruminative Response Scale [K-RRS]

The K-RRS includes 22 items assessing brooding, reflective pondering, and depressive rumination. The items are rated on a four-point Likert scale ranging from 1 (“almost never”) to 4 (“almost always”). Cronbach’s α ranged from .77 to .79 in previous studies [15], and from .81 to .90 in this study.

3) Korean Version of the Event Related Rumination Inventory [K-ERRI]

The K-ERRI includes 20 items rated on a four-point Likert scale (0=“not at all” to 3=“often”), assessing intrusive rumination (unwanted, repetitive thoughts about a specific experience), and deliberate rumination (intentional, purposeful thoughts aimed at understanding the event) following stressful events. The Cronbach’s α were .95 and .96 [16] and .93 and .94 in this study.

4) Columbia-Suicide Severity Rating Scale [C-SSRS]

The C-SSRS measures suicidal ideation (severity and intensity) and behavior. The severity subscale includes five yes/no items scored from 0 to 5 based on the most severe ideation endorsed. The intensity subscale includes five Likert scale items, whereas the suicidal behavior subscale includes five dichotomous items on preparatory behavior, attempts, actual suicide attempts, and non-suicidal self-injury. A suicide risk index (range: 1∼9) was computed based on a study by Nilsson et al. [17], excluding non-suicidal self-injury. The highest score, representing the most severe suicidal thoughts or behaviors endorsed, was used as the final suicide risk index. The Cronbach’s α for the Korean version was .74 for severity of ideation, .88 for intensity of ideation subscale, and .62 for suicidal behavior [18], and .86, .79, and .81, in this study, respectively.

5) Korean Version of the Interpersonal Need Questionnaire [K-INQ]

The 15-item K-INQ assesses perceived burdensomeness and thwarted belongingness on a seven-point Likert scale (1=“not at all true for me” to 7=“very true for me”). The Cronbach’s α for the K-INQ was .88 for perceived burdensomeness and .87 for thwarted belongingness [19], and in this study, the values were .95 and .92, respectively.

6) Patient Health Questionnaire-9 [PHQ-9]

The PHQ-9 screens for depression severity via nine items (0=“not at all” to 3=“nearly every day”), with total scores ranging from 0 to 27. Severity is classified as minimal (0∼4) to severe (20∼27) [20]. The Cronbach’s α of the Korean version was .95 [21] and .90 in this study.

7) Generalized Anxiety Disorder-7 [GAD-7]

The GAD-7 assesses anxiety severity using seven items (0=“not at all” to 3=“nearly every day”). The total scores range from 0 to 21. Severity is classified as minimal (0∼4) to severe (15∼21) [22]. The Cronbach’s α of the Korean version was .93 [23] and .90 in this study.

8) Attentional Control Questionnaire [ACQ]

The 20-item ACQ assesses attentional control on a four-point Likert scale (1=“almost never” to 4=“always”), with higher scores indicating greater attentional control. The Cronbach’s α of the Korean version was .84 [24] and .87 in this study.

9) Attentional bias probe task

Adapted from Abend et al. [25], this task used 40 word pairs (32 suicide-neutral and 8 neutral-neutral) to assess attention bias. Each trial included a fixation point (500 ms), word pair (500 ms), and probe (“＜” or “＞”) requiring directional response. In suicide-neutral trials, the probe appeared either in the suicide-related word position (congruent trials) or in the neutral word position (incongruent trials), whereas in neutral- neutral trials, probes were equally distributed between positions. The task comprised 160 trials: 64 congruent, 64 incongruent, and 32 neutral- neutral trials. Word and probe positions, and probe types were counterbalanced [25]. Trials with incorrect responses, extreme reaction times (＜200 ms or ＞3,000 ms), or outlier (±2.5 standard deviations from the participant’s mean) were excluded [26]. Participants with more than 50% incorrect responses were excluded from further analysis. Two attention bias scores, orientation and disengagement, were calculated based on the mean reaction times across conditions [27].

3. Statistical analyses

Descriptive statistics were employed to examine the participants’ demographic characteristics and the item properties of the K-SRS. Confirmatory factor analysis [CFA] evaluated the factor structure of the K-SRS, with model fit evaluated using χ² test (p＞.05), Comparative Fit Index (CFI ≥ .95), Root Mean Square Error of Approximation (RMSEA ≤.08), and Standardized Root Mean Square Residual (SRMR ≤.08) [28]. Owing to the poor fit of the single-factor model (Table 2), bifactor exploratory structural equation modeling [bifactor ESEM] was conducted based on Rogers et al. [13]. Unlike CFA, ESEM allows all items to load on multiple factors [29], providing a more realistic representation of factor structure by integrating the exploratory flexibility of EFA with the confirmatory rigor of CFA within a structural equation modeling framework. Unidimensionality was evaluated using bifactor ESEM indices, including explained common variance [ECV], percent of uncontaminated correlations [PUC], and average relative parameter bias [ARPB]. Thresholds of ≥.70 for ECV/PUC [30] and ＜10∼15% for ARPB [31] were employed to support unidimensionality. Given the evidence of multidimensionality and high modification indices between Items 1∼4 and Items 5∼8 (Table 3, 4), a two-factor ESEM was conducted, with model fit evaluated using the same fit indices as the CFA.

The validity analyses also included convergent validity, assessed via correlations between the K-SRS and other rumination measures (e.g., the K-RRS and K-ERRI), and discriminant validity, tested via correlations with the deliberate rumination factor of the K-ERRI. Concurrent validity was evaluated via correlations with suicide risk, suicidal ideation intensity, perceived burdensomeness, thwarted belongingness, depression, anxiety, attentional control, and attentional bias. Incremental validity was tested employing hierarchical regression analysis to determine whether the K-SRS explained suicide risk beyond depression, anxiety, perceived burdensomeness, and thwarted belongingness. Known-group validity was examined using a one-way analysis of variance [ANOVA] to compare the K-SRS scores across depression and anxiety severity levels. Internal consistency was assessed via Cronbach’s α and McDonald’s ω. A receiver operating characteristic [ROC] analysis was employed to determine the optimal cutoff score for identifying individuals with a history of suicide attempts. The Youden index (sensitivity+specificity−1) was used to determine the cutoff that maximized classification accuracy, and the cutoff yielding the maximum value of this statistic was identified as the optimal threshold [32]. The area under the ROC curve [AUC] was interpreted as acceptable (0.7∼0.8), excellent (0.8∼0.9), or outstanding (≥ 0.9) [33].

All the statistical analyses were conducted using Jamovi 2.3.28. The factor analyses were conducted using Mplus 8.11, and ROC analyses were conducted using the pROC package in R.

Results

1. Demographic characteristics and study variables by history of suicide attempt

Table 1 presents the demographic characteristics and comparisons of the study variables between participants with and without a history of suicide attempts. No significant differences were found in the demographic characteristics between the groups. However, participants with a history of suicide attempts reported significantly higher levels of suicide-specific rumination, depressive rumination, intrusive rumination, suicide risk, lifetime suicidal ideation intensity, perceived burdensomeness, depression, and anxiety compared with those without a history of suicide attempts. By contrast, brooding, reflective pondering, deliberate rumination, thwarted belongingness, attentional control, and attentional bias did not differ significantly between the groups.

2. Descriptive statistics, factor structure, and reliability of the K-SRS

All the eight items of the K-SRS satisfied the criteria for normality, with skewness values ranging from 0.02 to 0.86 (below the cutoff of 3) and kurtosis values ranging from −1.24 to −0.26 (below the cutoff of 10). The items also demonstrated strong item-total correlations (Table 5).

3. Factor structure

The CFA indicated poor model fit for the single-factor structure, χ²(20)=272.52, p＜.001; CFI=0.77; RMSEA=0.32, 90% CI [0.29, 0.35]; SRMR=0.13, suggesting that the unidimensional model did not adequately represent the data. Additionally, the ancillary bifactor indices from the bifactor-ESEM supported potential multidimensionality: ECV=0.71, PUC=0.57, and ARPB=29.9%. By contrast, the two-factor ESEM demonstrated a good model fit (Table 2), χ²(13)=18.36, p=.145; CFI=0.99; RMSEA=0.06, 90% CI [0.00, 0.11]; SRMR=0.01.

Factor 1 (items 1∼4) comprised items reflecting recurrent and intrusive suicidal ideation and was labeled intrusive rumination about suicide. Factor 2 (items 5∼8) reflected preoccupation with suicide methods, including imagery and planning, as illustrated by Item 5 (“Thinking about how I want to kill myself”) and Item 6 (“Imagine what killing myself with different methods would be like”). This factor was labeled contemplation of suicide methods. The inter-factor correlation was 0.70.

4. Reliability of the K-SRS

The internal consistency of the K-SRS was satisfactory, with a Cronbach’s α of .94 and a McDonald’s ω of .95 for the total scale. The intrusive rumination about suicide subscale demonstrated high reliability, with both Cronbach’s α and McDonald’s ω at .96. Similarly, the contemplation of suicide methods factor demonstrated strong internal consistency, with both indices at .93.

5. Validity of the K-SRS

1) Convergent and discriminant validity

The K-SRS total and subscale scores were significantly correlated with brooding (rs=.27∼.38), reflective pondering (rs=.19∼.35), depressive rumination on the K-RRS (rs=.48∼.57), and intrusive rumination on the K-ERRI (rs=.37∼.50), supporting convergent validity (Table 6). By contrast, deliberate rumination on the K-ERRI was not significantly correlated with the K-SRS scores (rs=−.01 to .01), supporting discriminant validity.

2) Concurrent validity

The K-SRS total and subscale scores were significantly correlated with suicide risk (rs=.57∼.62), lifetime suicidal ideation intensity (rs=.53∼.60), perceived burdensomeness (rs=.35∼.55), thwarted belongingness (rs=.35∼.48), depression (rs=.45∼.65), and anxiety (rs=.34∼.58), supporting concurrent validity.

The total score and the intrusive rumination about suicide factor were negatively correlated with attentional control (rs=−.39 to −.30), whereas the contemplation of suicide methods factor was not (r=−.17).

No significant associations were found between the K-SRS scores and attentional bias indices (orientation: rs=−.16 to −.08, ps＞.05; disengagement: rs=.11 to .18, ps＞.05). However, the intrusive rumination about suicide factor indicated a trend-level positive correlation with disengagement (r=.18, p=.051). This finding suggests that individuals with higher levels of intrusive rumination about suicide may disengage more slowly from suicide-related stimuli. The item-level analysis showed that Item 1 (“Cannot ‘turn off’ these thoughts”) and Item 3 (“Have trouble getting the suicidal thoughts out of my mind”) were significantly correlated with disengagement scores (r=.19, p=.037).

3) Incremental validity and known-group validity

The hierarchical regression analyses demonstrated that the K-SRS explained an additional 15.7% of the variance in suicide risk (ΔR²=.151, F(1, 113)=31.43, p＜.001), beyond established risk factors, including depression, anxiety, perceived burdensomeness, thwarted belongingness, and other rumination measures, supporting its incremental validity (Table 7).

Known-group validity was supported by significantly higher K-SRS total and subscale scores among individuals with clinical levels of depression and anxiety symptoms compared with those with minimal symptoms (Depression: F(3, 120)=19.19, p＜.001, η²=.32; Anxiety: F(2, 56.32)=14.09, p＜.001, η²=.24; Table 8).

6. Screening performance of the K-SRS

ROC analysis identified an optimal cutoff score of 13.5 on the K-SRS for distinguishing individuals with a history of suicide attempts (based on the C-SSRS). This cutoff yielded: sensitivity=0.68; specificity=0.75; and Youden’s J=0.42). The area under the curve [AUC] was 0.77 (95% CI [0.68, 0.85]), indicating acceptable classification accuracy [33].

Discussion

This study evaluated the psychometric properties of the K-SRS.

CFA indicated a poor fit for the single-factor structure, whereas the subsequent bifactor ESEM suggested multidimensionality. By contrast, the two-factor ESEM demonstrated a good model fit for the K-SRS. The first factor, intrusive rumination about suicide, included items capturing the difficulty in disengaging from suicidal thoughts, feelings of entrapment, and the inability to suppress such thoughts. The second factor, contemplation of suicide methods, comprised items reflecting thoughts about suicide planning, such as contemplating accessible or easy ways to die. This structure partially aligns with the bifactor model proposed during the initial development of the SRS, which included one general factor and two specific factors [13]. Similarly, in the validation study of the Farsi version of the SRS [14], the single-factor model demonstrated a poor fit in CFA, leading to the inclusion of correlated residuals. In that study, correlations between the residuals of items 5∼8 ranged from .37 to .47, suggesting that a single latent factor may not adequately explain these items and indicating the potential presence of a second factor. These findings underscore the need for further research to clarify the factor structure of the K-SRS across cultural contexts.

The K-SRS demonstrated strong convergent and discriminant validity. Significant positive correlations with brooding, reflective pondering, depressive rumination, and intrusive rumination (as measured by the K-RRS and K-ERRI) supported convergent validity, whereas a lack of significant association with deliberate rumination indicated discriminant validity. These findings suggest that suicide-specific rumination is linked to maladaptive, uncontrollable thought patterns, rather than intentional or neutral forms of rumination. This aligns with meta-analytic evidence indicating that brooding is strongly associated with suicidal ideation and suicide attempts [4]. Among the rumination subtypes, reflective pondering was more strongly correlated with the K-SRS total score and contemplation of suicide methods factor than with intrusive rumination about suicide. As reflective pondering is typically deliberate and problem-solving-oriented, with lower emotional involvement [15], contemplation of suicide methods factor likely reflects a more intentional form of rumination. The non-significant association between K-SRS and deliberate rumination further underscores the role of uncontrollability in suicide-specific rumination, suggesting that its involuntary nature can be particularly detrimental.

Furthermore, the K-SRS demonstrated strong concurrent validity, with higher levels of suicide-specific rumination associated with greater suicide risk, a higher intensity of suicidal ideation, and elevated levels of key suicide risk factors, including perceived burdensomeness, thwarted belongingness, depression, and anxiety. In addition, a negative association between intrusive rumination about suicide and attentional control was observed, consistent with previous findings linking rumination to poorer self-reported attentional control [34]. This suggests that individuals struggling with persistent suicidal thoughts may have difficulty in regulating their attention and reinforcing their perseverative thinking patterns. These findings highlight the potential benefits of interventions targeting attentional control, such as attention-bias modification training and mindfulness-based approaches, in reducing suicidal rumination.

Although the K-SRS scores were not significantly associated with orientation indices of attentional bias, a marginal association was observed between the intrusive rumination about suicide factor and disengagement scores, suggesting that individuals with more intrusive suicidal thoughts may struggle to shift their attention from suicide-related cues. Supporting this, Items 1 and 3, which reflect uncontrollable and persistent suicidal thoughts, were significantly associated with disengagement scores. These findings partially align with cognitive models of suicidal behavior, which emphasize the role of attentional biases in the development and maintenance of suicidal thinking [35]. Suicide-specific rumination has also been conceptualized as a proxy for suicide-specific attentional biases/fixations in intensive longitudinal investigations of the cognitive model of suicidal behavior [36]. However, the overall weak associations observed in this sample may reflect limitations inherent in probe-based tasks, which provide only static, momentary snapshots of attention [37], and may not adequately capture the dynamic and context-dependent nature of attentional processing in suicidal individuals [38]. Future research must explore alternative methodologies, such as mood-dependent administration or dynamic measures such as eye-tracking, to better assess suicide-related attentional bias [26].

The incremental validity of the K-SRS was supported by its ability to predict suicide risk beyond established risk factors including depression and anxiety, perceived burdensomeness, and thwarted belongingness [39]. Notably, difficulty in controlling suicidal thoughts has been shown to be a stronger predictor of future suicide attempts than depression or posttraumatic stress disorder in 3,916 individuals with suicidal ideation [8]. Collectively, these findings suggest that suicide-specific rumination is an independent and robust predictor of suicidal ideation and attempts.

The results also support the known-group validity of the K-SRS, with significantly higher total and subscale scores observed among individuals with greater severity of depression and anxiety. The strongest group differences emerged for the Intrusive Rumination subscale, indicating its sensitivity to clinical levels of depressive and anxiety symptoms, as intrusive rumination is a core feature of both conditions [40]. ROC analysis further assessed the discriminatory power of the K-SRS for identifying individuals with a history of suicide attempts. The optimal cutoff score was 13.5, with a sensitivity of 0.68 and specificity of 0.75 (AUC=.77), indicating an acceptable level of discrimination [33].

Despite these promising findings, several limitations must be noted. First, although the sample size (N=124) adhered to the minimum guidelines for a factor analysis of an 8-item scale [41], it was relatively small compared with typical validation studies. Cross-validation, which is typically recommended to evaluate the generalizability of factor structures, was not feasible owing to the challenges of recruiting individuals with suicidal ideation. To address this limitation, ESEM and bifactor-ESEM were employed to evaluate the robustness of the factor structure beyond CFA. However, these approaches cannot replace formal cross-validation. Future studies with larger sample sizes are needed to replicate the two-factor structure and further evaluate the reliability and validity of the K-SRS. Second, the cross-sectional design precluded the assessment of test-retest reliability or sensitivity to change, limiting the causal interpretation of the relationships between the study variables. Future longitudinal research is needed to evaluate whether suicide-specific rumination prospectively predicts suicide attempts among individuals with suicidal ideation. Third, the sample was predominantly female and comprised young adults, raising concerns about the generalizability of the scale to other demographic groups. Although measurement invariance across genders has been confirmed for the SRS [13,14], further validation with a larger and more diverse sample is required to establish the factor structure and overall validity of the measure across populations. Fourth, in the dot probe task, suicide-related words were rated as significantly more arousing than neutral words. This heightened arousal may have influenced the response patterns, potentially confounding the assessment of attentional bias. Future studies must carefully select neutral words with comparable arousal levels to ensure a more accurate assessment of attentional biases toward suicide-related stimuli.

In conclusion, the K-SRS is a reliable and valid tool for assessing rumination related to suicidal thoughts, intentions, and plans. Its robust psychometric properties support its utility in identifying individuals with a heightened suicide risk. However, further validation through longitudinal studies and more diverse samples is needed to strengthen its generalizability and applicability.

Notes

Acknowledgements

This article is a revised and supplemented version of the first author’s master’s thesis.

Conflicts of interest

The authors declared no conflict of interest.

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2023S1A5A2A03088002).

References

• 1. Statistics korea. Causes of death statistics in 2023 [Internet] Daejeon, Statstics Korea; 2024 [cited 2025 August 21]. Available from: https://kostat.go.kr/board.es?mid=a10301010000&bid=218&act=view&list_no=433106
• 2. Haregu T, Cho E, Spittal M, Armstrong G. The rate of transition to a suicide attempt among people with suicidal thoughts in the general population: A systematic review. Journal of Affective Disorders. 2023;331:57-63. https://doi.org/10.1016/j.jad.2023.03.063ArticlePubMed
• 3. May AM, Klonsky ED. What distinguishes suicide attempters from suicide ideators? A meta-analysis of potential factors. Clinical Psychology: Science and Practice. 2016;23:5-20. https://doi.org/10.1111/cpsp.12136Article
• 4. Rogers ML, Joiner TE. Rumination, suicidal ideation, and suicide attempts: A meta-analytic review. Review of General Psychology. 2017;21:132-142. https://doi.org/10.1037/gpr0000101Article
• 5. Miranda R, Nolen-Hoeksema S. Brooding and reflection: Rumination predicts suicidal ideation at 1-year follow-up in a community sample. Behaviour Research and Therapy. 2007;45:3088-95. https://doi.org/10.1016/j.brat.2007.07.015ArticlePubMedPMC
• 6. Polanco-Roman L, Jurska J, Quiñones V, Miranda R. Brooding, reflection, and distraction: Relation to non-suicidal self-injury versus suicide attempts. Archives of Suicide Research. 2015;19:350-365. https://doi.org/10.1080/13811118.2014.981623ArticlePubMedPMC
• 7. Miranda R, Ortin A, Scott M, Shaffer D. Characteristics of suicidal ideation that predict the transition to future suicide attempts in adolescents. Journal of Child Psychology and Psychiatry. 2014;55:1288-1296. https://doi.org/10.1111/jcpp.12245ArticlePubMedPMC
• 8. Nock MK, Millner AJ, Joiner TE, Gutierrez PM, Han G, Hwang I, et al. Risk factors for the transition from suicide ideation to suicide attempt: Results from the army study to assess risk and resilience in servicemembers (Army STARRS). Journal of Abnormal Psychology. 2018;127:139-149. https://doi.org/10.1037/abn0000317ArticlePubMedPMC
• 9. Rogers ML, Gorday JY, Joiner TE. Examination of characteristics of ruminative thinking as unique predictors of suicide-related outcomes. Journal of Psychiatric Research. 2021;139:1-7. https://doi.org/10.1016/j.jpsychires.2021.05.001ArticlePubMed
• 10. Rogers ML, Bozzay ML, Hughes CD, Schatten HT, Armey MF. Examining the predictive utility of suicidal ideation characteristics in relation to real‐time monitoring of suicidal ideation and suicide attempts at follow‐up. Suicide and Life‐Threatening Behavior. 2024;54:982-992. https://doi.org/10.1111/sltb.13103ArticlePubMedPMC
• 11. Nolen-Hoeksema S. Responses to depression and their effects on the duration of depressive episodes. Journal of Abnormal Psychology. 1991;100:569-582. https://doi.org/10.1037/0021-843X.100.4.569ArticlePubMed
• 12. Morrison R, O'Connor RC. A systematic review of the relationship between rumination and suicidality. Suicide and Life-Threatening Behavior. 2008;38:523-538. https://doi.org/10.1521/suli.2008.38.5.523ArticlePubMed
• 13. Rogers ML, Law KC, Houtsma C, Tucker RP, Anestis MD, Joiner TE. Development and initial validation of a scale assessing suicide-specific rumination: The suicide rumination scale. Assessment. 2022;29:1777-1794. https://doi.org/10.1177/10731911211033897ArticlePubMed
• 14. Sahlan RN, Grunewald W, Smith AR. Validations of the Farsi versions of the depressive symptom inventory suicidality subscale (F-DSI-SS) and suicide rumination scale (F-SRS) among Iranian college students. Suicide and Life-Threatening Behavior. 2023;53:16-28. https://doi.org/10.1111/sltb.12917ArticlePubMed
• 15. Kim SJ, Kim JH, Youn SC. Validation of the Korean-ruminative response scale(K-RRS). Korean Journal of Clinical Psychology. 2010;29:1-19. https://doi.org/10.15842/kjcp.2010.29.1.001Article
• 16. Ahn HN, Joo HS, Sim KS, Min JW. Validation of the event related rumination inventory in a Korean population. Cognitive Behavior Therapy in Korea. 2013;13:149-72.
• 17. Nilsson ME, Suryawanshi S, Gassmann-Mayer C, Dubrava S, McSorley P, Jiang K. Columbia-suicide severity rating scale scoring and data analysis guide [Internet]. CSSRS Scoring Version; 2013 [cited 2025 August 21]. Available from: https://cssrs.columbia.edu/wp-content/uploads/ScoringandDataAnalysisGuide-for-Clinical-Trials-1.pdf
• 18. Jang HA, Park EH, Jon DI, Park HJ, Hong HJ, Jung MH, et al. Validation of the Columbia suicide severity rating scale in depression patients. Korean Journal of Clinical Psychology. 2014;33:799-814. https://doi.org/10.15842/kjcp.2014.33.4.008Article
• 19. Lee HY, Lee JA, Oh KS. Validation of the Korean version of interpersonal needs questionnaire (K-INQ) for older adults. Korean Journal of Clinical Psychology. 2015;34:291-313. https://doi.org/10.15842/kjcp.2015.34.1.014Article
• 20. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine. 2001;16:606-613. https://doi.org/10.1046/j.1525-1497.2001.016009606.xArticlePubMedPMC
• 21. An JY, Seo ER, Lim KH, Shin JH, Kim JB. Standardization of the Korean version of screening tool for depression(Patient Health Questionnaire-9, PHQ-9). Journal of the Korean Society of Biological Therapies in Psychiatry. 2013;19:47-56.
• 22. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine. 2006;166:1092-1097. https://doi.org/10.1001/archinte.166.10.1092ArticlePubMed
• 23. Ahn JK, Kim Y, Choi KH. The psychometric properties and clinical utility of the Korean version of GAD-7 and GAD-2. Frontiers in Psychiatry. 2019;10:127. https://doi.org/10.3389/fpsyt.2019.00127ArticlePubMedPMC
• 24. Yoon SA, Kim HT, Choi JS. Reliability and validity of an attentional control questionnaire. Korean Journal of Clinical Psychology. 2007;26:185-199. https://doi.org/10.15842/kjcp.2007.26.1.011Article
• 25. Abend R, Pine DS, Bar-Haim Y. The TAU-NIMH attention bias measurement toolbox [Internet]. 2014 [cited 2025 August 21]. Available from: https://people.socsci.tau.ac.il/mu/ybh/files/2023/04/Protocol.pdf
• 26. Rogers ML, Carosa CL, Haliczer LA, Hughes CD, Schofield CA, Armey MF. The suicide dot probe task: Psychometric properties and validity in relation to suicide-related outcomes. Suicide and Life-Threatening Behavior. 2023;53:1010-1024. https://doi.org/10.1111/sltb.12999ArticlePubMed
• 27. Koster EHW, Crombez G, Verschuere B, De Houwer J. Selective attention to threat in the dot probe paradigm: Differentiating vigilance and difficulty to disengage. Behaviour Research and Therapy. 2004;42:1183-1192. https://doi.org/10.1016/j.brat.2003.08.001ArticlePubMed
• 28. Hu Lt, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal. 1999;6:1-55. https://doi.org/10.1080/10705519909540118Article
• 29. Asparouhov T, Muthén B. Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal. 2009;16:397-438. https://doi.org/10.1080/10705510903008204Article
• 30. Rodriguez A, Reise SP, Haviland MG. Applying bifactor statistical indices in the evaluation of psychological measures. Journal of Personality Assessment. 2016;98:223-237. https://doi.org/10.1080/00223891.2015.1089249ArticlePubMed
• 31. Muthén B, Kaplan D, Hollis M. On structural equation modeling with data that are not missing completely at random. Psychometrika. 1987;52:431-462. https://doi.org/10.1007/BF02294365Article
• 32. Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32-35. https://doi.org/10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3ArticlePubMed
• 33. Hosmer DW, Lemeshow S. Applied logistic regression. 2nd ed. Wiley New York; 2000.
• 34. DeJong H, Fox E, Stein A. Does rumination mediate the relationship between attentional control and symptoms of depression? Journal of Behavior Therapy and Experimental Psychiatry. 2019;63:28-35. https://doi.org/10.1016/j.jbtep.2018.12.007ArticlePubMedPMC
• 35. Wenzel A, Beck AT. A cognitive model of suicidal behavior: Theory and treatment. Applied and Preventive Psychology. 2008;12:189-201. https://doi.org/10.1016/j.appsy.2008.05.001Article
• 36. Rogers ML, Law KC, Lawrence OC, Mandel AA. Perseveration on suicidal thoughts and images in daily life: An examination of the cognitive model of suicide through a dynamic systems lens. Behaviour Research and Therapy. 2024;177:104524. https://doi.org/10.1016/j.brat.2024.104524ArticlePubMed
• 37. Coleshill MJ, Sharpe L, Colagiuri B. No evidence that attentional bias towards pain-related words is associated with verbally induced nocebo hyperalgesia: A dot-probe study. PAIN Reports. 2021;6:e921. https://doi.org/10.1097/PR9.0000000000000921ArticlePubMedPMC
• 38. Todd J, van Ryckeghem DML, Sharpe L, Crombez G. Attentional bias to pain-related information: A meta-analysis of dot-probe studies. Health Psychology Review. 2018;12:419-436. https://doi.org/10.1080/17437199.2018.1521729ArticlePubMed
• 39. Van Orden KA, Witte TK, Cukrowicz KC, Braithwaite SR, Selby EA, Joiner TE. The interpersonal theory of suicide. Psychological Review. 2010;117:575-600. https://doi.org/10.1037/a0018697ArticlePubMedPMC
• 40. Nolen-Hoeksema S. The role of rumination in depressive disorders and mixed anxiety/depressive symptoms. Journal of Abnormal Psychology. 2000;109:504-511. https://doi.org/10.1037/0021-843X.109.3.504ArticlePubMed
• 41. Kline RB. Principles and practice of structural equation modeling. 3rd ed. The Guilford Press; 2011.

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