Discussion Enhancing Occupational Rehabilitation in High-Stress Work: How Recovery Experience Reduces Dysfunctional Attitudes Through Emotional Regulation

Discussion Enhancing Occupational Rehabilitation in High-Stress Work: How Recovery Experience Reduces Dysfunctional Attitudes Through Emotional Regulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Discussion Enhancing Occupational Rehabilitation in High-Stress Work: How Recovery Experience Reduces Dysfunctional Attitudes Through Emotional Regulation Xiang Ji, Hairong Yu, Yu Zuo, Yiya Xu, Miaomiao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8749993/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 4 You are reading this latest preprint version Abstract Background: Surveying and mapping personnel operate in high-demand, precision-driven environments characterized by chronic occupational stress, posing significant risks to cognitive health and work functioning. Dysfunctional attitudes—rigid, perfectionistic cognitive schemas—constitute a key vulnerability factor for depression, anxiety, and impaired occupational adaptation in this population. Prior research has primarily described stressors rather than explained how daily psychological recovery mitigates such maladaptive cognition, a gap with direct implications for occupational rehabilitation. To bridge this divide between macro-stress descriptions and micro-cognitive mechanisms, this study introduces an integrative theoretical perspective that links organizational psychology (recovery theory) with clinical cognitive vulnerability models. To address this, a chain mediation model was tested to examine whether and how recovery experience influences dysfunctional attitudes through regulatory emotional self-efficacy and anxiety sensitivity. Methods: A sample of 107 field surveying personnel completed questionnaires assessing recovery experience (REQ), dysfunctional attitudes (DAS), regulatory emotional self-efficacy (RESE), and anxiety sensitivity (ASI-3). Data were analyzed using correlation analysis and structural equation modeling (SEM) to test direct and mediating effects, with bootstrap confidence intervals calculated for indirect effects. Results: SEM indicated that: (1) recovery experience directly and negatively predicted dysfunctional attitudes; (2) regulatory emotional self-efficacy partially mediated this relationship; (3) anxiety sensitivity also partially mediated the relationship; and (4) regulatory emotional self-efficacy and anxiety sensitivity sequentially mediated the link between recovery experience and dysfunctional attitudes via a chain pathway. Bootstrap analyses confirmed the robustness of these indirect effects. Conclusion: This study provides an integrative framework connecting organizational psychology (recovery theory) with clinical psychology (cognitive vulnerability models) and occupational rehabilitation, systematically revealing the mechanism from external resource replenishment to internal cognitive schema improvement. Critically, it elucidates a sequential psychological pathway—from resource recovery, through enhanced emotion-regulation belief, to reduced catastrophic anxiety appraisal, and finally to attenuated dysfunctional cognition—that offers novel multi-level intervention targets for occupational rehabilitation. The findings highlight that interventions aimed at enhancing recovery quality, fostering emotional regulation beliefs, and correcting catastrophic interpretations of anxiety can help prevent and alleviate dysfunctional attitudes, thereby boosting individual psychological resilience and occupational functioning. These findings suggest integrated interventions for occupational rehabilitation programs targeting recovery, emotion regulation, and anxiety sensitivity. Health sciences/Health care Biological sciences/Psychology Social science/Psychology Recovery Experience Dysfunctional Attitudes Regulatory Emotional Self-Efficacy Anxiety Sensitivity Occupational Rehabilitation Surveying Personnel Figures Figure 1 Figure 2 Introduction Surveying and mapping personnel work in chronically demanding environments, often involving prolonged fieldwork in remote or challenging locations. Their tasks integrate high-stakes data acquisition, processing, and innovation under conditions of high cognitive load, stringent precision requirements, and relative isolation[1]. This occupational profile engenders a confluence of distinctive stressors: prolonged social isolation due to remote postings, sustained cognitive vigilance mandated by zero-error tolerance in data precision, and physiological challenges from operating in harsh or variable climates[1, 2]. Consequently, this group faces significant occupational stressors that can impair work capacity and necessitate rehabilitation. Such a constellation of persistent cognitive, social, and physical demands positions surveying personnel as an ideal population for examining the interplay between resource depletion, recovery processes, and the development of stable cognitive vulnerabilities relevant to occupational health. Extensive evidence confirms that chronic stress undermines mental health, increasing risks of fatigue, depression, and anxiety disorders[2,3], all of which are central concerns in occupational health and rehabilitation. Young professionals, in particular, experience compounded pressures from heavy workloads, strict management, and confined environments, leading to heightened psychological maladjustment and increased vulnerability to work disability[4]. Without timely intervention, these issues can escalate into sustained burnout, diminished cognitive functioning, and potential safety incidents[5], underscoring the need for effective rehabilitation strategies. Therefore, clarifying the protective and risk mechanisms underlying psychological adaptation in this workforce is crucial for developing targeted occupational rehabilitation programs. However, a significant gap exists in shifting focus from macro-level stress outcomes to micro-level psychological processes that influence rehabilitation potential. Existing research has primarily described the external stressors inherent to surveying work and their broad health impacts, without delineating the specific psychological pathways through which stress affects cognitive adaptation and, consequently, occupational functioning. While substantial literature documents associations between occupational stress and negative affective states (e.g., depression, anxiety) or behavioral outcomes like burnout[3, 4], there is a relative paucity of research explicating how chronic stress shapes deeper, stable cognitive schemas, such as dysfunctional attitudes. For high-pressure occupations, dysfunctional attitudes represent a particularly detrimental vulnerability factor. These rigid cognitive schemas are closely associated with depression and anxiety and interact dynamically with persistent job demands, reducing cognitive flexibility, promoting maladaptive coping strategies[6], and thereby exacerbating burnout and hindering rehabilitation progress[7]. Although the detrimental role of dysfunctional attitudes is recognized, prior studies have mainly described correlations between stressors and cognitive outcomes, without systematically uncovering the mediating mechanisms that translate stress exposure into irrational cognitive patterns that can obstruct return to work or full occupational engagement. Thus, a critical leap from describing correlations to elucidating mechanistic pathways is essential for designing targeted, evidence-based interventions within occupational rehabilitation frameworks. In short, while high-pressure work is linked to negative cognition, the malleable psychological processes that transmit or buffer this effect remain unclear. This gap limits the development of targeted, evidence-based interventions within occupational rehabilitation frameworks. Recovery experience—the process of replenishing psychological resources during non-work time through psychological detachment, relaxation, mastery, and control[8]—offers a promising theoretical lens relevant to occupational rehabilitation. Grounded in Conservation of Resources Theory[9] and the Effort-Recovery Model[10]—which posits that psychophysiological systems strained by work effort require periods of non-demand to restore baseline functioning and prevent cumulative strain — adequate off-job recovery is essential for counteracting work stress, maintaining cognitive function, and preserving work ability—a core goal of rehabilitation. Insufficient recovery perpetuates the depletion of finite cognitive resources, particularly executive functions like cognitive flexibility and inhibition, creating fertile ground for the emergence and entrenchment of rigid, automatic thinking patterns[5]. Empirical evidence confirms that insufficient recovery is linked to emotional exhaustion, cognitive fatigue, and poorer work outcomes[11]. Nevertheless, how recovery experience specifically influences deeper, stable cognitive schemas such as dysfunctional attitudes, which are critical barriers to successful occupational adaptation, remains poorly understood. Particularly for surveying personnel and similar high-stress occupations, it is unclear whether recovery exerts a direct effect on cognition or functions indirectly by empowering other psychological resources that facilitate rehabilitation. To address this shortcoming, the present study focused on proactive, individual-level psychological regulatory resources. Specifically, regulatory emotional self-efficacy and anxiety sensitivity were proposed as theoretically significant mediators within a rehabilitation context. Regulatory emotional self-efficacy refers to an individual’s confidence in managing their own emotions, a core protective belief in stress adaptation and a key resource for coping with work demands during rehabilitation[12,13]. Rooted in Bandura's social cognitive theory[12], this construct is bolstered by mastery experiences—successful episodes of managing one's affective states. High-quality recovery may provide such mastery experiences (e.g., successfully using relaxation to mitigate work-related irritation), thereby strengthening beliefs in one's capability to regulate emotions in future challenging work situations[6]. Anxiety sensitivity denotes the fear of anxiety-related sensations and their catastrophic misinterpretation, a stable risk factor for anxiety disorders that can severely limit occupational participation and rehabilitation engagement[14,15]. It is crucial to distinguish anxiety sensitivity from state anxiety; the former is a trait-like cognitive fear of fear that amplifies the distress and perceived threat of normal stress responses, directly contributing to the maintenance of avoidance behaviors and maladaptive beliefs (e.g., "losing control is catastrophic") in work contexts[7]. Recovery experiences may enhance regulatory emotional self-efficacy, thereby strengthening perceived control over stressful emotions[16]. This, in turn, could reduce catastrophic appraisals of anxiety—that is, lower anxiety sensitivity[17]—ultimately buffering the development of dysfunctional cognitive patterns that impede work adjustment. The theoretical link between regulatory emotional self-efficacy (RESE) and anxiety sensitivity (ANXS) is pivotal: individuals with higher RESE are more likely to appraise anxiety sensations as manageable challenges rather than uncontrollable threats, thereby directly reducing the fear component intrinsic to anxiety sensitivity[8]. While prior studies separately indicate that recovery activities can improve emotion regulation capacity[18] and reduce anxiety sensitivity[19], research integrating these variables within a coherent sequential mediation model among high-stress occupational groups, with explicit links to rehabilitation outcomes, is lacking. Therefore, this study aimed to systematically examine whether and how recovery experience influences dysfunctional attitudes via regulatory emotional self-efficacy and anxiety sensitivity. This study extends recovery research into occupational rehabilitation by elucidating how daily recovery can mitigate cognitive vulnerability in high-stress professions, thereby informing more holistic rehabilitation approaches. Based on this theoretical foundation, and the proposed sequential logic wherein recovery fosters emotion-regulation beliefs (RESE) that mitigate catastrophic fear of anxiety (ANXS), thereby protecting against rigid cognitive schemas (DAS), the following hypotheses were formulated: H1: Recovery experience directly and negatively predicts dysfunctional attitudes among surveying and mapping personnel. H2: Regulatory emotional self-efficacy mediates the relationship between recovery experience and dysfunctional attitudes. H3: Anxiety sensitivity mediates the relationship between recovery experience and dysfunctional attitudes. H4: Regulatory emotional self-efficacy and anxiety sensitivity serially mediate the relationship between recovery experience and dysfunctional attitudes. Methods Participants A convenience sample of 115 personnel engaged in long-term field surveying and mapping tasks was recruited. After rigorous screening, eight invalid responses were excluded, resulting in 107 valid questionnaires (effective rate: 93.0%). The sample comprised 100 males (93.5%) and 7 females (6.5%), with a mean age of 28.4 years ( SD = 5.7). The sample had an average occupational tenure in surveying of 6.2 years ( SD = 4.1). Their typical work cycle involved extended field deployments, often lasting 10 to 15 consecutive days, followed by shorter rest periods of 2 to 4 days. This gender imbalance reflects the current demographic composition of field surveying teams in the region and may limit the generalizability of findings to more gender-balanced occupational groups. It is acknowledged that this imbalance may particularly affect the generalizability of findings related to emotion regulation and anxiety, constructs for which gender differences are frequently reported in the literature[9]. Participants held various positions (e.g., 65 technicians, 25 team leaders/supervisors, 12 project coordinators, and 5 data processing specialists) and were currently involved in field operations such as measurement, data collection, and equipment maintenance. Inclusion criteria were: current employment in field surveying with continuous field operations over the past six months; age ≥18 years; voluntary participation with written informed consent; and ability to comprehend questionnaire items. Exclusion criteria included a missing data rate >10% for key variables, clear patterned responses, or independent invalidation judgment by two psychology-trained researchers. Measures Recovery Experience Questionnaire (REQ): The 16-item scale by Sonnentag and Fritz[8] assessed four dimensions: psychological detachment, relaxation, mastery, and control (5-point Likert). Higher scores indicated better recovery. The REQ is a well-established measure whose factor structure and validity have been consistently supported in occupational stress research, including studies involving demanding professional groups[8, 11]. Cronbach's α in the present sample was 0.91 (subscales: 0.77 - 0.89). Regulatory Emotional Self-Efficacy Scale (RESE): The Chinese version[13,20] includes 11 items across three dimensions: expressing positive emotions, managing despondency/distress, and regulating anger/irritation (5-point Likert). Higher scores indicated stronger self-efficacy. This scale has demonstrated robust psychometric properties across diverse cultures and occupational groups, including those in high-stress professions, making it suitable for the present study[13, 10]. Cronbach's α was 0.92 (subscales: 0.84 - 0.94). Anxiety Sensitivity Index-3 (ASI-3): The 18-item scale by Taylor et al.[14] measured catastrophic beliefs about anxiety sensations across physical, cognitive, and social concerns (5-point Likert). Higher scores indicated greater sensitivity. The ASI-3 is a gold-standard measure with extensive validation in clinical and non-clinical populations, and its three-factor structure has been consistently supported, ensuring its appropriateness for assessing cognitive vulnerability in occupational settings[14, 11]. Cronbach's α was 0.95 (subscales: 0.87- 0.95). Dysfunctional Attitude Scale (DAS): The Chinese version[21,22] includes 40 items covering eight cognitive factors (e.g., perfectionism, need for approval) on a 5-point Likert scale (10 items reverse-scored). Higher scores indicated more severe dysfunctional attitudes. The DAS has been widely used and validated in samples experiencing work-related stress and is considered a relevant instrument for capturing rigid cognitive schemas in high-pressure occupational contexts[6, 12]. Cronbach's α was 0.89 (subscales: 0.71 - 0.86). Procedure The study received approval from the participating organization's management and the relevant institutional research ethics committee. Participants received detailed information about the study’s purpose, voluntary nature, confidentiality, and academic use, with assurance that results were unrelated to performance evaluations. After providing written informed consent, anonymous questionnaires were administered collectively during scheduled training or rest periods. A researcher was present to clarify items. Completion time averaged 15–20 minutes. Analytic Strategy Data were analyzed using SPSS 26.0 and AMOS 24.0. After conducting descriptive statistics and Pearson correlations, a structural equation modeling (SEM) approach was used to test the hypothesized chain mediation model. The maximum likelihood estimation method was employed. To enhance the robustness of mediation effect testing, bias-corrected bootstrap confidence intervals (CIs) were calculated based on 5000 bootstrap samples. Model modifications were guided by modification indices (MIs) and theoretical justification. Specifically, three dimensions from the DAS (compulsivity, autonomous attitudes, cognitive philosophy) and two from the REQ (psychological detachment, mastery) were removed from the final model because they showed non-significant loadings and cross-loadings, and their removal improved model fit without altering the theoretical meaning of the core constructs, thereby avoiding overfitting and maintaining parsimony. This model-trimming approach was principled: the removed dimensions consistently showed the highest MIs for cross-loadings and the weakest standardized loadings (< .50) in the initial model. Their exclusion was theoretically justifiable as they represented more peripheral facets of the core constructs (e.g., 'compulsivity' within DAS is conceptually narrower than the core 'perfectionism' factor relevant to work stress; 'psychological detachment' in REQ, while important, may operate differently in continuously remote field settings). This process aimed not for statistical convenience but for achieving a more parsimonious and theoretically coherent model with superior fit, focusing on the central dimensions most pertinent to the research hypotheses[13]. Results Common Method Bias Test Harman’s single-factor test was conducted. An unrotated exploratory factor analysis yielded 19 factors with eigenvalues >1. The first factor accounted for 44.3% of the variance. Given the self-report nature of the data, a method factor was incorporated into the subsequent structural equation model to statistically control for potential common method bias. Descriptive Statistics and Correlation Analysis Means, standard deviations, and Pearson correlations are presented in Table 1. Dysfunctional attitudes averaged in the low-to-moderate range ( M = 2.60, SD = 0.57). Recovery experience was positively correlated with dysfunctional attitudes ( r = .26, p < .01), regulatory emotional self-efficacy ( r = .91, p < .001), and anxiety sensitivity ( r = .46, p < .001). Regulatory emotional self-efficacy was positively correlated with dysfunctional attitudes ( r = .21, p < .01). Anxiety sensitivity showed a strong positive correlation with dysfunctional attitudes ( r = .81, p < .001). Regulatory emotional self-efficacy and anxiety sensitivity were positively correlated ( r = .38, p < .001). Demographic variables showed no significant correlation with core study variables and were not controlled for in further analyses. Table 1 Descriptive Statistics and Correlations Among Variables Variable M SD 1 2 3 4 5 1 Gender 1.07 0.25* 2 Grade 2.17 0.51 -0.24* 3 RECX 3.24 1.00 0.08 0.03 4 RESE 3.28 1.08 0.05 0.11 0.91** 5 ANXS 2.57 0.99 0.01 -0.11 0.46** 0.38** 6 DYSF 2.60 0.57 -0.08 -0.40 0.26* 0.21* 0.81** Note. M = mean; SD = standard deviation. Gender was coded as 1 = male, 2 = female. Variable abbreviations: RECX = Recovery Experience; RESE = Regulatory Emotional Self-Efficacy; ANXS = Anxiety Sensitivity; DYSF = Dysfunctional Attitudes. *p < .05, **p < .01. The correlation matrix revealed two patterns warranting preliminary discussion. First, recovery experience (RECX) showed a significant positive correlation with dysfunctional attitudes (DYSF) ( r = .26), which appears counterintuitive to its hypothesized protective role. This surface-level association may reflect a third-variable effect common in cross-sectional stress research: individuals experiencing high concurrent stress and cognitive strain might simultaneously report a heightened perceived need for recovery and endorse more dysfunctional cognitions, leading to a positive correlation that does not represent a direct causal influence[14]. The subsequent SEM analysis, which controls for the interrelationships among variables, is designed to clarify the true predictive relationship. Second, the very high correlation between recovery experience and regulatory emotional self-efficacy (RESE) ( r = .91) underscores their conceptual affinity, as successful recovery likely builds beliefs in one's regulatory capabilities[6]. To ensure this collinearity did not unduly bias parameter estimates in the path model, we examined variance inflation factors (VIFs). All VIF values were well below the conservative threshold of 5 (max VIF = 2.1), indicating that multicollinearity was not at a level that would compromise the stability or interpretation of the regression coefficients, thus supporting their inclusion as distinct constructs in the model[15]. Main Effect and Chain Mediation Effect Test A chain mediation structural equation model was constructed with recovery experience as the exogenous variable, regulatory emotional self-efficacy and anxiety sensitivity as mediators, and dysfunctional attitudes as the endogenous variable (Figure 1). The initial model fit was suboptimal. Guided by modification indices and theoretical coherence, three non-significant dimensions from the DAS (compulsivity, autonomous attitudes, cognitive philosophy) and two from the REQ (psychological detachment, mastery) were removed. The modified model showed good fit: χ²/df = 1.904, RMSEA = 0.078, CFI = 0.980, TLI = 0.973, NFI = 0.955, RFI = 0.937 (Table 2). Table 2 Fit Indices for the Chain Mediation Model Fit Index Acceptable Range Model Result Evaluation PCMIN/DF 1～3 1.904 Good RMSEA <0.08 0.078 Good SRMR 0.90 0.941 Good NFI >0.90 0.955 Good RFI >0.90 0.937 Good IFI >0.90 0.981 Good TLI >0.90 0.973 Good CFI >0.90 0.980 Good Standardized path coefficients for the final model are shown in Figure 2. Recovery experience positively predicted anxiety sensitivity ( β = 0.68, p < .001) and regulatory emotional self-efficacy ( β = 0.97, p < .001). Regulatory emotional self-efficacy negatively predicted anxiety sensitivity ( β = -0.28, p < .001). Anxiety sensitivity positively predicted dysfunctional attitudes ( β = 0.90, p < .05). The direct path from recovery experience to dysfunctional attitudes was negative and significant ( β = -0.43, p < .001). Mediation effects were calculated using the product of coefficients method and validated with bootstrap 95% confidence intervals (CIs): Indirect via RESE alone: 0.97 × 0.36 = 0.349, 95% CI [0.211, 0.512]. Indirect via ANXS alone: 0.68 × 0.90 = 0.612, 95% CI [0.398, 0.854]. Chain mediation (RECX→RESE→ANXS→DYSF): 0.97 × (-0.28) × 0.90 = -0.244, 95% CI [-0.381, -0.125]. The total effect was 1.635. The direct effect accounted for 26.30% of the total effect; total indirect effects accounted for 73.70%. Within indirect effects, the path via anxiety sensitivity contributed the most (37.43%), followed by the path via regulatory emotional self-efficacy (21.35%). The chain mediation path contributed a negative proportion (14.93%). All bootstrap CIs excluding zero confirmed the significance of the indirect effects. This chain pathway elucidates that the protective effect of recovery on cognitive vulnerability operates through a sequential psychological process: first by empowering individuals’ belief in their emotion regulation capacity (RESE), which subsequently reduces catastrophic appraisals of anxiety (ANXS), thereby mitigating the development of dysfunctional attitudes. These results supported H2, H3, and H4. Discussion This study validated the direct negative predictive effect of recovery experience on dysfunctional attitudes (H1) among surveying personnel. The findings align with Conservation of Resources Theory[9] and the Effort-Recovery Model[10], and resonate with occupational rehabilitation literature emphasizing the role of resource replenishment in maintaining work ability and preventing disability[23]. In high-load surveying work, persistent resource depletion without adequate recovery can lead to cognitive exhaustion and reduced flexibility[24], key factors in diminished occupational performance. This extends the Effort-Recovery Model by specifying its cognitive consequences: insufficient recovery not only fails to restore energetic resources but may also entrench maladaptive cognitive patterns that actively hinder rehabilitation. Sufficient recovery through detachment, relaxation, mastery, and control appears to diminish rigid cognitive schemas that can hinder adaptive work behavior and successful rehabilitation, resonating with research linking recovery to reduced cognitive rigidity[25]. From a neurocognitive perspective, adequate recovery—particularly psychological detachment—may facilitate this process by reducing sustained activation in the prefrontal cortex, a region critical for executive control and top-down emotion regulation[16]. This ‘quieting’ of hypervigilant neural circuits could restore cognitive elasticity and dampen the automatic activation of ingrained, irrational beliefs[26], processes paramount for cognitive rehabilitation in occupational contexts. For organizations and rehabilitation professionals, this underscores the importance of ensuring high-quality recovery opportunities as a preventive and rehabilitative measure, such as advocating for standardized work hours, encouraging complete work disconnection, and providing recreational resources. The results confirmed that regulatory emotional self-efficacy partially mediated the relationship (H2). This reveals a key mechanism: recovery enhances confidence in managing emotions, a critical psychological resource during occupational stress and rehabilitation. According to social cognitive theory, successful recovery provides mastery experiences that boost regulatory emotional self-efficacy. Specifically, the ‘mastery’ and ‘control’ dimensions of recovery may be pivotal for fostering RESE, as they provide concrete experiences of successfully managing one’s time and activities outside of work, which can generalize to beliefs about managing internal states[8, 6]. Higher self-efficacy not only predisposes individuals to use adaptive strategies like cognitive reappraisal rather than maladaptive ones like rumination when facing negative emotions[17] but, as per the process model of emotion regulation[17], may also influence the earlier stage of appraisal . Individuals with high RESE are likely to appraise work challenges as more manageable and less threatening from the outset, thereby preventing the initiation of the stress-cognition maladaptive cascade that culminates in dysfunctional attitudes[18]. This positions regulatory emotional self-efficacy as a bridge connecting external resource recovery to internal cognitive vulnerability. Interventions for surveying personnel, and within occupational rehabilitation programs more broadly, should therefore include skill-based training to enhance this self-efficacy, empowering individuals to disrupt the path from insufficient recovery to cognitive dysfunction and impaired work adjustment. The study also verified that anxiety sensitivity partially mediated the link (H3). Recovery benefits are partly achieved by reducing catastrophic interpretations of anxiety-related signals, which are known to exacerbate work avoidance and impede engagement in rehabilitation activities[24]. High-quality recovery, particularly deep detachment and relaxation, can lower physiological arousal and attention to anxiety symptoms, attenuating catastrophic beliefs[15]. Here, the ‘relaxation’ dimension of recovery may be especially potent in directly countering the somatic hyper-awareness central to anxiety sensitivity[19]. Lower anxiety sensitivity implies greater tolerance for internal discomfort and reduced secondary anxiety, weakening the maintenance of irrational beliefs tied to anxiety, such as the need for absolute control[20]—beliefs often observed in individuals struggling to return to demanding work. This finding bridges recovery research with transdiagnostic cognitive models, positioning anxiety sensitivity not merely as a risk factor for anxiety disorders but as a core maintenance mechanism for a broad spectrum of work-related psychological distress, including depression and burnout[20]. Thus, targeting anxiety sensitivity through recovery-oriented interventions holds significant promise for holistic occupational rehabilitation. Interventions within a rehabilitation context could include psychoeducation to normalize anxiety responses combined with guided recovery practice to directly lower anxiety sensitivity, thereby reducing a key barrier to occupational participation. The core finding was the confirmation of the chain mediating effect (H4): recovery experience → regulatory emotional self-efficacy → anxiety sensitivity → dysfunctional attitudes. This aligns with the extended process model of emotion regulation[17], wherein belief in one’s regulatory ability influences the appraisal of specific emotions. Individuals with high regulatory emotional self-efficacy are more confident in managing anxiety, less likely to appraise it as dangerous, and thus exhibit lower anxiety sensitivity[12,17]. Reduced anxiety sensitivity, in turn, mitigates the cognitive rigidity associated with fear of losing control, a common theme in work-related stress and rehabilitation. This sequential pathway—from external resource replenishment (recovery), to internal belief empowerment (RESE), to maladaptive appraisal revision (ANXS), and finally to deep-seated cognitive schema change (DAS)—provides a fine-grained map of how environmental support translates into lasting cognitive adaptation. It represents a significant theoretical integration, organically concatenating constructs from organizational behavior (recovery theory), social-cognitive theory (self-efficacy), clinical psychology (anxiety sensitivity), and cognitive therapy (dysfunctional schemas)[21]. This chain mechanism demonstrates that the cognitive-protective effect of recovery is realized through an orderly psychological process highly relevant to occupational rehabilitation: first enhancing emotion regulation resources (a key rehabilitation target), then altering the maladaptive cognitive appraisal of the emotional experience, and finally impacting deeper cognitive schemas that affect work behavior. This suggests that comprehensive support for high-stress workers and rehabilitation clients should be synergistic: creating conditions for recovery while prioritizing training to enhance regulatory emotional self-efficacy, subsequently lowering anxiety sensitivity to prevent dysfunctional attitudes and promote sustainable work functioning. Practical Implications for Rehabilitation The present findings advocate for a multi-level, sequential intervention framework to mitigate cognitive vulnerability and promote work adaptation in high-stress, mobile occupations like surveying. Organizational Level: Institutionalizing Recovery Promotion. Organizations must move beyond generic employee assistance programs. For field-based personnel, this entails implementing enforceable policies such as mandatory “digital disconnection” periods after work hours, guaranteeing minimum rest periods following extended field deployments (e.g., 10-15 consecutive days), and equipping remote bases with facilities that actively foster relaxation and mastery (e.g., libraries, workshops for non-work skills)[22]. This creates the essential pre-condition for resource replenishment. Individual Skill Level: Targeted Emotion Regulation Capacity Building. Occupational safety and rehabilitation programs should embed evidence-based modules focused on building emotion regulation skills, such as mindfulness-based stress reduction or cognitive reappraisal training[23]. The critical aim is to facilitate “mastery experiences” in managing work-related affective states, thereby concretely elevating RESE and disrupting the link between strain and cognitive rigidity. Cognitive Restructuring Level: Normalizing and Reframing Anxiety Appraisals. Through psychoeducation, workers can learn to differentiate normative stress-induced anxiety from catastrophic fear of anxiety sensations. Cognitive-behavioral techniques can then be used to challenge catastrophic interpretations of somatic cues (e.g., “My heart is racing, so I must be losing control”) directly targeting and reducing anxiety sensitivity[24]. This final layer consolidates gains by modifying the core cognitive risk factor. These three tiers are synergistic: an organizational culture that legitimizes recovery provides the psychological space for skill acquisition; enhanced self-efficacy builds resilience and creates readiness for cognitive change; and modified appraisals solidify rehabilitation outcomes and prevent relapse. Limitations and Future Directions Several limitations should be noted. First, the cross-sectional design precludes causal inferences. Future research should employ longitudinal or experimental designs, ideally tracking individuals through periods of stress, recovery, and occupational rehabilitation. Experience sampling or diary methods would be particularly valuable for capturing the dynamic, within-person fluctuations in recovery experiences, daily regulatory self-efficacy, anxiety sensitivity, and cognitive states across typical work-rest cycles[25]. Second, the sample was drawn from a single occupational group with a significant gender imbalance, potentially limiting generalizability. Future studies should test the model in broader populations, including other high-stress occupations, and balance gender representation. Third, reliance on self-report measures introduces potential bias, despite statistical controls. Incorporating physiological indicators (e.g., heart rate variability as an index of recovery), behavioral measures of cognitive flexibility, or supervisor ratings of work adjustment would strengthen findings[26]. Finally, the model focused on intra-individual processes. Future research could incorporate contextual factors highly relevant to occupational rehabilitation, like organizational support, supervisor behavior, and team climate, to build a more comprehensive person-situation interaction model. Furthermore, future studies could explicitly link these psychological pathways to concrete occupational rehabilitation outcomes, such as return-to-work success, work engagement, or productivity, by incorporating longitudinal data from rehabilitation tracking systems. Conclusion By testing a chain mediation model, this study systematically elucidated the psychological mechanism linking recovery experience to dysfunctional attitudes among surveying personnel, with clear implications for occupational rehabilitation. The findings confirmed that recovery experience negatively predicts dysfunctional attitudes both directly and indirectly through the independent and sequential mediating roles of regulatory emotional self-efficacy and anxiety sensitivity. This research makes a seminal theoretical contribution by providing the first integrative model that delineates a chain mechanism connecting organizational recovery processes with clinical cognitive vulnerability. It bridges the longstanding divide between macro-stress descriptions in occupational health and micro-cognitive mechanisms in clinical psychology, offering a coherent pathway from resource depletion to schema entrenchment. This research represents a significant interdisciplinary integration, mapping a sequential transmission pathway relevant to maintaining work ability: from external resource replenishment (recovery experience) to internal emotion regulation belief (regulatory emotional self-efficacy), to the specific cognitive appraisal of an emotional state (anxiety sensitivity), and finally to a deep-seated cognitive schema (dysfunctional attitude) that can hinder occupational adaptation. This “Resource-Belief-Appraisal-Schema” (RBAS) pathway offers a nuanced, dynamic framework for understanding the plasticity of psychological adaptation and the genesis of vulnerability in high-pressure occupations, moving beyond static correlational findings to a process-oriented explanation. Practically, the findings compel a paradigm shift in occupational rehabilitation, from passively managing psychological symptoms to proactively constructing a robust, multi-tiered mental health promotion system centered on “resource replenishment, empowered regulation, and adaptive cognition.” It suggests an integrated, three-pronged intervention approach for occupational rehabilitation: creating and protecting recovery opportunities, empowering emotion regulation beliefs through skill training, and correcting anxiety-related catastrophic cognitions via psychoeducation. This multi-level strategy can synergistically prevent and mitigate dysfunctional attitudes at their source, thereby enhancing individual psychological resilience and promoting organizational safety, efficacy, and successful work rehabilitation. Consequently, we advocate for policy and practice that institutionalizes recovery-supportive environments, integrates emotion-regulation mastery into core competency training, and embeds cognitive-restructuring techniques into routine occupational health screenings. For occupational rehabilitation practitioners, this model provides a multi-level framework for designing targeted psychological support programs for high-stress occupational groups. Ultimately, this work calls for a systemic re-imagining of occupational rehabilitation, positioning it not merely as a remedial service but as a strategic investment in building cognitively flexible, resilient, and sustainably productive workforces. Abbreviations RECX Recovery Experience DETC Psychological Detachment RELA Relaxation Experience MAST Mastery Experience CTRL Control Experience RESE Regulatory Emotional Self-Efficacy POSF SE in Expressing Positive Emotion DESM SE in Managing Despondency/Distress ANGM SE in Regulating Anger/Irritation ANXS Anxiety Sensitivity PHYC Physical Concerns COGC Cognitive Concerns SOCC Social Concerns DYSF Dysfunctional Attitudes ANIR Anger/Irritation DEPR Distress/Depression POEM Positive Emotion VULN Vulnerability ATRP Attraction and Repulsion PERF Perfectionism COMP Compulsivity APPR Need for Approval DEPN Dependency AUTN Autonomous Attitudes COGP Cognitive Philosophy Declarations Acknowledgements The authors wish to express their sincere gratitude to the surveying and mapping personnel who participated in this study. Their willingness to share their experiences was fundamental to this research. We gratefully acknowledge the work of scholars whose foundational research in occupational rehabilitation psychology—particularly concerning work ability, return-to-work processes, and the management of anxiety in occupational contexts—informed the conceptual framing and practical implications of this study. Finally, we thank our colleagues for their constructive feedback during the development of this manuscript. Sincerely, Xiang Ji On behalf of all co-authors Ethical approval and exemption statement This study was reviewed by the institutional research ethics committee responsible for the oversight of social science research involving human participants. In accordance with institutional and national guidelines for minimal-risk research, the study was determined to qualify for exemption from full ethical review (waiver of formal ethics committee approval). Exempting body: Institutional research ethics committee (name and contact details available upon request). Exemption date: 02 December 2025. Exemption number/ID: Not applicable – the institution does not issue numeric approval codes for exempt social science research; official exemption documentation is available upon request. Reason for exemption: The research involves only anonymous questionnaire surveys with adult participants; no intervention, invasive procedure, or experimental manipulation is conducted; no sensitive personal information is collected; all participants provide written informed consent; the study poses no more than minimal risk to participants. These characteristics meet the exemption criteria specified in the Declaration of Helsinki and the institutional policy on ethical review of human participant research. All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent Written informed consent was obtained from all individual participants included in this study. Prior to the commencement of data collection, each participant was presented with a detailed information sheet outlining the study's purpose, procedures, potential risks and benefits, data confidentiality measures, and their right to withdraw. Consent was explicitly obtained by the principal investigator on 02 December 2025, with signed consent forms securely archived by the corresponding author. These documents are available for review by the editors or ethics committee upon formal request. Local Ethics Approval This study received specific approval from the local ethics review authority at the institution where the research was conducted—the Institutional Review Board of University, Shandong, China. The research protocol was reviewed and approved by this local committee in accordance with its jurisdiction and operational guidelines. The study was conducted in full compliance with all applicable local regulatory and ethical requirements for human participant research. Data Availability Statement The datasets generated and/or analysed during the current study, along with all associated materials necessary for replication, are available in the Figshare repository. This curated archive includes de-identified raw and processed data files, comprehensive documentation of the experimental procedure and variable scoring, electronic copies of the administered questionnaires, and the complete original output files from all statistical analyses (SPSS and AMOS). Access is provided via the following permanent DOI: https://doi.org/10.6084/m9.figshare.31302328. The data are publicly available under the terms of the repository's access protocol. For any specific inquiries regarding the data, reasonable requests can be directed to the corresponding author. References Ioannou, L. G., Foster, J., Morris, N. B., Piil, J. F., Havenith, G., Mekjavic, I. B., & Flouris, A. D. (2022). Occupational heat strain in outdoor workers: A comprehensive review and meta-analysis. Temperature, 9 (1), 67–102. https://doi.org/10.1080/23328940.2022.2030634 Alroomi, A. S., & Mohamed, S. (2021). The impact of job isolation on the psychological well-being and safety performance of remote workers in the oil and gas industry. Safety Science, 133 , 105001. https://doi.org/10.1016/j.ssci.2020.105001 Koutsimani, P., Montgomery, A., & Georganta, K. (2019). The relationship between burnout, depression, and anxiety: A systematic review and meta-analysis. Frontiers in Psychology, 10 , 284. https://doi.org/10.3389/fpsyg.2019.00284 Bakker, A. B., & Demerouti, E. (2018). Multiple levels in job demands-resources theory: Implications for employee well-being and performance. In E. Diener, S. Oishi, & L. Tay (Eds.), Handbook of well-being. DEF Publishers. Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology, 74 (5), 1252–1265. https://doi.org/10.1037/0022-3514.74.5.1252 Binnewies, C., Sonnentag, S., & Mojza, E. J. (2010). Recovery during the weekend and fluctuations in weekly job performance: A week-level study examining intra-individual relationships. Journal of Occupational and Organizational Psychology, 83 (2), 419–441. https://doi.org/10.1348/096317909X418049 Newcombe, B., Olthuis, J. V., & Giberson, E. R. (2026). A brief workplace intervention for anxiety sensitivity aimed at reducing the risk of posttraumatic stress in first responders. Cognitive Behaviour Therapy, 55 (2), 271–291. https://doi.org/10.1080/16506073.2025.2491467 Wu, Q., Ran, G., & Zhang, Q. (2022). Rejection sensitivity and trait anxiety: The indirect effects of regulatory emotional self-efficacy and shyness. Current Psychology, 41(8) , 5481–5490. https://doi.org/10.1007/s12144-020-01070-y Nolen-Hoeksema, S. (2012). Emotion regulation and psychopathology: The role of gender. Annual Review of Clinical Psychology, 8 , 161–187. https://doi.org/10.1146/annurev-clinpsy-032511-143109 Li, C., Shi, K., Luo, Z., & Li, L. (2013). Regulatory emotional self-efficacy and work-family conflict: Testing a mediated moderation model in Chinese nurses. Journal of Advanced Nursing, 69 (11), 2457–2468. https://doi.org/10.1111/jan.12121 Osman, A., Gutierrez, P. M., Smith, K., Fang, Q., Lozano, G., & Devine, A. (2010). The Anxiety Sensitivity Index–3: Analyses of dimensions, reliability estimates, and correlates in nonclinical samples. Journal of Personality Assessment, 92 (1), 45–52. https://doi.org/10.1080/00223890903379332 De Graaf, L. E., Roelofs, J., & Huibers, M. J. H. (2009). Measuring dysfunctional attitudes in the general population: The Dysfunctional Attitude Scale (form A) revised. Cognitive Therapy and Research, 33 (4), 345–355. https://doi.org/10.1007/s10608-009-9229-y Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Press. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88 (5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). Springer. Lim, J., Teng, J., Wong, K. F., & Chee, M. W. L. (2016). Modulating rest-break length induces differential recruitment of automatic and controlled attentional processes upon task reengagement. NeuroImage, 134 , 64–73. https://doi.org/10.1016/j.neuroimage.2016.03.077 Gross, J. J. (1998). The emerging field of emotion regulation: An integrative review. Review of General Psychology, 2 (3), 271–299. https://doi.org/10.1037/1089-2680.2.3.271 Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping . Springer Publishing Company. Manzoni, G. M., Pagnini, F., Castelnuovo, G., & Molinari, E. (2008). Relaxation training for anxiety: A ten-years systematic review with meta-analysis. BMC Psychiatry, 8 , 41. https://doi.org/10.1186/1471-244X-8-41 Taylor, S. (2014). Anxiety sensitivity: Theory, research, and treatment of the fear of anxiety . Routledge. Adkins, J. A. (1999). Promoting organizational health: The evolving practice of occupational health psychology. Professional Psychology: Research and Practice, 30 (2), 129–137. https://doi.org/10.1037/0735-7028.30.2.129 Knight, C., Patterson, M., & Dawson, J. (2019). Building work engagement through organizational resources: A longitudinal study examining the roles of person-organization fit and organizational identification. Journal of Occupational Health Psychology, 24 (4), 415–427. https://doi.org/10.1037/ocp0000139 Vonderlin, R., Biermann, M., Bohus, M., & Lyssenko, L. (2020). Mindfulness-based programs in the workplace: A meta-analysis of randomized controlled trials. Mindfulness, 11 (7), 1579–1598. https://doi.org/10.1007/s12671-020-01328-3 Smits, J. A. J., Berry, A. C., Tart, C. D., & Powers, M. B. (2008). The efficacy of cognitive-behavioral interventions for reducing anxiety sensitivity: A meta-analytic review. Behaviour Research and Therapy, 46 (9), 1047–1054. https://doi.org/10.1016/j.brat.2008.06.010 Sonnentag, S. (2001). Work, recovery activities, and individual well-being: A diary study. Journal of Occupational Health Psychology, 6 (3), 196–210. https://doi.org/10.1037/1076-8998.6.3.196 Greiner, B. A., Krause, N., Ragland, D., & Fisher, J. M. (2004). Occupational stressors and hypertension: A multi-method study using observer-based job analysis and self-reports in urban transit operators. Social Science & Medicine, 59 (5), 1081–1094. https://doi.org/10.1016/j.socscimed.2003.12.006 Additional Declarations No competing interests reported. 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Their tasks integrate high-stakes data acquisition, processing, and innovation under conditions of high cognitive load, stringent precision requirements, and relative isolation[1]. This occupational profile engenders a confluence of distinctive stressors: prolonged social isolation due to remote postings, sustained cognitive vigilance mandated by zero-error tolerance in data precision, and physiological challenges from operating in harsh or variable climates[1, 2]. Consequently, this group faces significant occupational stressors that can impair work capacity and necessitate rehabilitation. Such a constellation of persistent cognitive, social, and physical demands positions surveying personnel as an ideal population for examining the interplay between resource depletion, recovery processes, and the development of stable cognitive vulnerabilities relevant to occupational health. Extensive evidence confirms that chronic stress undermines mental health, increasing risks of fatigue, depression, and anxiety disorders[2,3], all of which are central concerns in occupational health and rehabilitation. Young professionals, in particular, experience compounded pressures from heavy workloads, strict management, and confined environments, leading to heightened psychological maladjustment and increased vulnerability to work disability[4]. Without timely intervention, these issues can escalate into sustained burnout, diminished cognitive functioning, and potential safety incidents[5], underscoring the need for effective rehabilitation strategies. Therefore, clarifying the protective and risk mechanisms underlying psychological adaptation in this workforce is crucial for developing targeted occupational rehabilitation programs.\u003c/p\u003e\n\u003cp\u003eHowever, a significant gap exists in shifting focus from macro-level stress outcomes to micro-level psychological processes that influence rehabilitation potential. Existing research has primarily described the external stressors inherent to surveying work and their broad health impacts, without delineating the specific psychological pathways through which stress affects cognitive adaptation and, consequently, occupational functioning. While substantial literature documents associations between occupational stress and negative affective states (e.g., depression, anxiety) or behavioral outcomes like burnout[3, 4], there is a relative paucity of research explicating \u003cem\u003ehow\u003c/em\u003e chronic stress shapes deeper, stable cognitive schemas, such as dysfunctional attitudes. For high-pressure occupations, dysfunctional attitudes represent a particularly detrimental vulnerability factor. These rigid cognitive schemas are closely associated with depression and anxiety and interact dynamically with persistent job demands, reducing cognitive flexibility, promoting maladaptive coping strategies[6], and thereby exacerbating burnout and hindering rehabilitation progress[7]. Although the detrimental role of dysfunctional attitudes is recognized, prior studies have mainly described correlations between stressors and cognitive outcomes, without systematically uncovering the mediating mechanisms that translate stress exposure into irrational cognitive patterns that can obstruct return to work or full occupational engagement. Thus, a critical leap from \u003cem\u003edescribing correlations\u003c/em\u003e to \u003cem\u003eelucidating mechanistic pathways\u003c/em\u003e is essential for designing targeted, evidence-based interventions within occupational rehabilitation frameworks. In short, while high-pressure work is linked to negative cognition, the malleable psychological processes that transmit or buffer this effect remain unclear. This gap limits the development of targeted, evidence-based interventions within occupational rehabilitation frameworks.\u003c/p\u003e\n\u003cp\u003eRecovery experience\u0026mdash;the process of replenishing psychological resources during non-work time through psychological detachment, relaxation, mastery, and control[8]\u0026mdash;offers a promising theoretical lens relevant to occupational rehabilitation. Grounded in Conservation of Resources Theory[9] and the Effort-Recovery Model[10]\u0026mdash;which posits that psychophysiological systems strained by work effort require periods of non-demand to restore baseline functioning and prevent cumulative strain\u003cstrong\u003e\u0026mdash;\u003c/strong\u003eadequate off-job recovery is essential for counteracting work stress, maintaining cognitive function, and preserving work ability\u0026mdash;a core goal of rehabilitation. Insufficient recovery perpetuates the depletion of finite cognitive resources, particularly executive functions like cognitive flexibility and inhibition, creating fertile ground for the emergence and entrenchment of rigid, automatic thinking patterns[5]. Empirical evidence confirms that insufficient recovery is linked to emotional exhaustion, cognitive fatigue, and poorer work outcomes[11]. Nevertheless, how recovery experience specifically influences deeper, stable cognitive schemas such as dysfunctional attitudes, which are critical barriers to successful occupational adaptation, remains poorly understood. Particularly for surveying personnel and similar high-stress occupations, it is unclear whether recovery exerts a direct effect on cognition or functions indirectly by empowering other psychological resources that facilitate rehabilitation.\u003c/p\u003e\n\u003cp\u003eTo address this shortcoming, the present study focused on proactive, individual-level psychological regulatory resources. Specifically, regulatory emotional self-efficacy and anxiety sensitivity were proposed as theoretically significant mediators within a rehabilitation context. Regulatory emotional self-efficacy refers to an individual\u0026rsquo;s confidence in managing their own emotions, a core protective belief in stress adaptation and a key resource for coping with work demands during rehabilitation[12,13]. Rooted in Bandura\u0026apos;s social cognitive theory[12], this construct is bolstered by mastery experiences\u0026mdash;successful episodes of managing one\u0026apos;s affective states. High-quality recovery may provide such mastery experiences (e.g., successfully using relaxation to mitigate work-related irritation), thereby strengthening beliefs in one\u0026apos;s capability to regulate emotions in future challenging work situations[6]. Anxiety sensitivity denotes the fear of anxiety-related sensations and their catastrophic misinterpretation, a stable risk factor for anxiety disorders that can severely limit occupational participation and rehabilitation engagement[14,15]. It is crucial to distinguish anxiety sensitivity from state anxiety; the former is a trait-like cognitive \u003cem\u003efear of fear\u003c/em\u003e that amplifies the distress and perceived threat of normal stress responses, directly contributing to the maintenance of avoidance behaviors and maladaptive beliefs (e.g., \u0026quot;losing control is catastrophic\u0026quot;) in work contexts[7]. Recovery experiences may enhance regulatory emotional self-efficacy, thereby strengthening perceived control over stressful emotions[16]. This, in turn, could reduce catastrophic appraisals of anxiety\u0026mdash;that is, lower anxiety sensitivity[17]\u0026mdash;ultimately buffering the development of dysfunctional cognitive patterns that impede work adjustment. The theoretical link between regulatory emotional self-efficacy (RESE) and anxiety sensitivity (ANXS) is pivotal: individuals with higher RESE are more likely to appraise anxiety sensations as manageable challenges rather than uncontrollable threats, thereby directly reducing the fear component intrinsic to anxiety sensitivity[8]. While prior studies separately indicate that recovery activities can improve emotion regulation capacity[18] and reduce anxiety sensitivity[19], research integrating these variables within a coherent sequential mediation model among high-stress occupational groups, with explicit links to rehabilitation outcomes, is lacking. Therefore, this study aimed to systematically examine whether and how recovery experience influences dysfunctional attitudes via regulatory emotional self-efficacy and anxiety sensitivity. This study extends recovery research into occupational rehabilitation by elucidating how daily recovery can mitigate cognitive vulnerability in high-stress professions, thereby informing more holistic rehabilitation approaches.\u003c/p\u003e\n\u003cp\u003eBased on this theoretical foundation, and the proposed sequential logic wherein recovery fosters emotion-regulation beliefs (RESE) that mitigate catastrophic fear of anxiety (ANXS), thereby protecting against rigid cognitive schemas (DAS), the following hypotheses were formulated:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1:\u003c/strong\u003e Recovery experience directly and negatively predicts dysfunctional attitudes among surveying and mapping personnel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2:\u003c/strong\u003e Regulatory emotional self-efficacy mediates the relationship between recovery experience and dysfunctional attitudes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3:\u003c/strong\u003e Anxiety sensitivity mediates the relationship between recovery experience and dysfunctional attitudes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4:\u003c/strong\u003e Regulatory emotional self-efficacy and anxiety sensitivity serially mediate the relationship between recovery experience and dysfunctional attitudes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA convenience sample of 115 personnel engaged in long-term field surveying and mapping tasks was recruited. After rigorous screening, eight invalid responses were excluded, resulting in 107 valid questionnaires (effective rate: 93.0%). The sample comprised 100 males (93.5%) and 7 females (6.5%), with a mean age of 28.4 years (\u003cem\u003eSD\u003c/em\u003e = 5.7). The sample had an average occupational tenure in surveying of 6.2 years (\u003cem\u003eSD\u003c/em\u003e = 4.1). Their typical work cycle involved extended field deployments, often lasting 10 to 15 consecutive days, followed by shorter rest periods of 2 to 4 days. This gender imbalance reflects the current demographic composition of field surveying teams in the region and may limit the generalizability of findings to more gender-balanced occupational groups. It is acknowledged that this imbalance may particularly affect the generalizability of findings related to emotion regulation and anxiety, constructs for which gender differences are frequently reported in the literature[9]. Participants held various positions (e.g., 65 technicians, 25 team leaders/supervisors, 12 project coordinators, and 5 data processing specialists) and were currently involved in field operations such as measurement, data collection, and equipment maintenance. Inclusion criteria were: current employment in field surveying with continuous field operations over the past six months; age \u0026ge;18 years; voluntary participation with written informed consent; and ability to comprehend questionnaire items. Exclusion criteria included a missing data rate \u0026gt;10% for key variables, clear patterned responses, or independent invalidation judgment by two psychology-trained researchers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRecovery Experience Questionnaire (REQ):\u003c/em\u003e\u003c/strong\u003e The 16-item scale by Sonnentag and Fritz[8] assessed four dimensions: psychological detachment, relaxation, mastery, and control (5-point Likert). Higher scores indicated better recovery. The REQ is a well-established measure whose factor structure and validity have been consistently supported in occupational stress research, including studies involving demanding professional groups[8, 11]. Cronbach\u0026apos;s \u0026alpha; in the present sample was 0.91 (subscales: 0.77 - 0.89).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRegulatory Emotional Self-Efficacy Scale (RESE):\u003c/em\u003e\u003c/strong\u003e The Chinese version[13,20] includes 11 items across three dimensions: expressing positive emotions, managing despondency/distress, and regulating anger/irritation (5-point Likert). Higher scores indicated stronger self-efficacy. This scale has demonstrated robust psychometric properties across diverse cultures and occupational groups, including those in high-stress professions, making it suitable for the present study[13, 10]. Cronbach\u0026apos;s \u0026alpha; was 0.92 (subscales: 0.84 - 0.94).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnxiety Sensitivity Index-3 (ASI-3):\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eThe 18-item scale by Taylor et al.[14] measured catastrophic beliefs about anxiety sensations across physical, cognitive, and social concerns (5-point Likert). Higher scores indicated greater sensitivity. The ASI-3 is a gold-standard measure with extensive validation in clinical and non-clinical populations, and its three-factor structure has been consistently supported, ensuring its appropriateness for assessing cognitive vulnerability in occupational settings[14, 11]. Cronbach\u0026apos;s \u0026alpha; was 0.95 (subscales: 0.87- 0.95).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDysfunctional Attitude Scale (DAS):\u003c/em\u003e\u003c/strong\u003e The Chinese version[21,22] includes 40 items covering eight cognitive factors (e.g., perfectionism, need for approval) on a 5-point Likert scale (10 items reverse-scored). Higher scores indicated more severe dysfunctional attitudes. The DAS has been widely used and validated in samples experiencing work-related stress and is considered a relevant instrument for capturing rigid cognitive schemas in high-pressure occupational contexts[6, 12]. Cronbach\u0026apos;s \u0026alpha; was 0.89 (subscales: 0.71 - 0.86).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received approval from the participating organization\u0026apos;s management and the relevant institutional research ethics committee. Participants received detailed information about the study\u0026rsquo;s purpose, voluntary nature, confidentiality, and academic use, with assurance that results were unrelated to performance evaluations. After providing written informed consent, anonymous questionnaires were administered collectively during scheduled training or rest periods. A researcher was present to clarify items. Completion time averaged 15\u0026ndash;20 minutes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytic Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using SPSS 26.0 and AMOS 24.0. After conducting descriptive statistics and Pearson correlations, a structural equation modeling (SEM) approach was used to test the hypothesized chain mediation model. The maximum likelihood estimation method was employed. To enhance the robustness of mediation effect testing, bias-corrected bootstrap confidence intervals (CIs) were calculated based on 5000 bootstrap samples. Model modifications were guided by modification indices (MIs) and theoretical justification. Specifically, three dimensions from the DAS (compulsivity, autonomous attitudes, cognitive philosophy) and two from the REQ (psychological detachment, mastery) were removed from the final model because they showed non-significant loadings and cross-loadings, and their removal improved model fit without altering the theoretical meaning of the core constructs, thereby avoiding overfitting and maintaining parsimony. This model-trimming approach was principled: the removed dimensions consistently showed the highest MIs for cross-loadings and the weakest standardized loadings (\u0026lt; .50) in the initial model. Their exclusion was theoretically justifiable as they represented more peripheral facets of the core constructs (e.g., \u0026apos;compulsivity\u0026apos; within DAS is conceptually narrower than the core \u0026apos;perfectionism\u0026apos; factor relevant to work stress; \u0026apos;psychological detachment\u0026apos; in REQ, while important, may operate differently in continuously remote field settings). This process aimed not for statistical convenience but for achieving a more parsimonious and theoretically coherent model with superior fit, focusing on the central dimensions most pertinent to the research hypotheses[13].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCommon Method Bias Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHarman\u0026rsquo;s single-factor test was conducted. An unrotated exploratory factor analysis yielded 19 factors with eigenvalues \u0026gt;1. The first factor accounted for 44.3% of the variance. Given the self-report nature of the data, a method factor was incorporated into the subsequent structural equation model to statistically control for potential common method bias.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive Statistics and Correlation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeans, standard deviations, and Pearson correlations are presented in Table 1. Dysfunctional attitudes averaged in the low-to-moderate range (\u003cem\u003eM\u003c/em\u003e = 2.60, \u003cem\u003eSD\u003c/em\u003e = 0.57). Recovery experience was positively correlated with dysfunctional attitudes (\u003cem\u003er\u003c/em\u003e = .26, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), regulatory emotional self-efficacy (\u003cem\u003er\u003c/em\u003e = .91, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and anxiety sensitivity (\u003cem\u003er\u003c/em\u003e = .46, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Regulatory emotional self-efficacy was positively correlated with dysfunctional attitudes (\u003cem\u003er\u003c/em\u003e = .21, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01). Anxiety sensitivity showed a strong positive correlation with dysfunctional attitudes (\u003cem\u003er\u003c/em\u003e = .81, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Regulatory emotional self-efficacy and anxiety sensitivity were positively correlated (\u003cem\u003er\u003c/em\u003e = .38, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Demographic variables showed no significant correlation with core study variables and were not controlled for in further analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e \u003cem\u003eDescriptive Statistics and Correlations Among Variables\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1\u0026nbsp;Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.25*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2\u0026nbsp;Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.24*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3\u0026nbsp;RECX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4 RESE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.91**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5 ANXS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e6 DYSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.26*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.21*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.81**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eM\u003c/em\u003e = mean; \u003cem\u003eSD\u003c/em\u003e = standard deviation. Gender was coded as 1 = male, 2 = female. Variable abbreviations: RECX = Recovery Experience; RESE = Regulatory Emotional Self-Efficacy; ANXS = Anxiety Sensitivity; DYSF = Dysfunctional Attitudes.\u003cem\u003e*p\u003c/em\u003e \u0026lt; .05, \u003cem\u003e**p\u003c/em\u003e \u0026lt; .01.\u003c/p\u003e\n\u003cp\u003eThe correlation matrix revealed two patterns warranting preliminary discussion. First, recovery experience (RECX) showed a significant positive correlation with dysfunctional attitudes (DYSF) (\u003cem\u003er\u003c/em\u003e = .26), which appears counterintuitive to its hypothesized protective role. This surface-level association may reflect a third-variable effect common in cross-sectional stress research: individuals experiencing high concurrent stress and cognitive strain might simultaneously report a heightened perceived need for recovery \u003cem\u003eand\u003c/em\u003e endorse more dysfunctional cognitions, leading to a positive correlation that does not represent a direct causal influence[14]. The subsequent SEM analysis, which controls for the interrelationships among variables, is designed to clarify the true predictive relationship. Second, the very high correlation between recovery experience and regulatory emotional self-efficacy (RESE) (\u003cem\u003er\u003c/em\u003e = .91) underscores their conceptual affinity, as successful recovery likely builds beliefs in one\u0026apos;s regulatory capabilities[6]. To ensure this collinearity did not unduly bias parameter estimates in the path model, we examined variance inflation factors (VIFs). All VIF values were well below the conservative threshold of 5 (max VIF = 2.1), indicating that multicollinearity was not at a level that would compromise the stability or interpretation of the regression coefficients, thus supporting their inclusion as distinct constructs in the model[15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain Effect and Chain Mediation Effect Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA chain mediation structural equation model was constructed with recovery experience as the exogenous variable, regulatory emotional self-efficacy and anxiety sensitivity as mediators, and dysfunctional attitudes as the endogenous variable (Figure 1).\u003c/p\u003e\n\u003cp\u003eThe initial model fit was suboptimal. Guided by modification indices and theoretical coherence, three non-significant dimensions from the DAS (compulsivity, autonomous attitudes, cognitive philosophy) and two from the REQ (psychological detachment, mastery) were removed. The modified model showed good fit: \u0026chi;\u0026sup2;/df = 1.904, RMSEA = 0.078, CFI = 0.980, TLI = 0.973, NFI = 0.955, RFI = 0.937 (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Fit Indices for the Chain Mediation Model\u003c/em\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eFit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eAcceptable Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eModel Result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eEvaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003ePCMIN/DF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e1～3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e1.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026lt;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026lt;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eRFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eStandardized path coefficients for the final model are shown in Figure 2. Recovery experience positively predicted anxiety sensitivity (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.68, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and regulatory emotional self-efficacy (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.97, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Regulatory emotional self-efficacy negatively predicted anxiety sensitivity (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.28, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Anxiety sensitivity positively predicted dysfunctional attitudes (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.90, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). The direct path from recovery experience to dysfunctional attitudes was negative and significant (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.43, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMediation effects were calculated using the product of coefficients method and validated with bootstrap 95% confidence intervals (CIs):\u003c/p\u003e\n\u003cp\u003eIndirect via RESE alone: 0.97 \u0026times; 0.36 = 0.349, 95% CI [0.211, 0.512].\u003c/p\u003e\n\u003cp\u003eIndirect via ANXS alone: 0.68 \u0026times; 0.90 = 0.612, 95% CI [0.398, 0.854].\u003c/p\u003e\n\u003cp\u003eChain mediation (RECX\u0026rarr;RESE\u0026rarr;ANXS\u0026rarr;DYSF): 0.97 \u0026times; (-0.28) \u0026times; 0.90 = -0.244, 95% CI [-0.381, -0.125].\u003c/p\u003e\n\u003cp\u003eThe total effect was 1.635. The direct effect accounted for 26.30% of the total effect; total indirect effects accounted for 73.70%. Within indirect effects, the path via anxiety sensitivity contributed the most (37.43%), followed by the path via regulatory emotional self-efficacy (21.35%). The chain mediation path contributed a negative proportion (14.93%). All bootstrap CIs excluding zero confirmed the significance of the indirect effects. This chain pathway elucidates that the protective effect of recovery on cognitive vulnerability operates through a sequential psychological process: first by empowering individuals\u0026rsquo; belief in their emotion regulation capacity (RESE), which subsequently reduces catastrophic appraisals of anxiety (ANXS), thereby mitigating the development of dysfunctional attitudes. These results supported H2, H3, and H4.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study validated the direct negative predictive effect of recovery experience on dysfunctional attitudes (H1) among surveying personnel. The findings align with Conservation of Resources Theory[9] and the Effort-Recovery Model[10], and resonate with occupational rehabilitation literature emphasizing the role of resource replenishment in maintaining work ability and preventing disability[23]. In high-load surveying work, persistent resource depletion without adequate recovery can lead to cognitive exhaustion and reduced flexibility[24], key factors in diminished occupational performance. This extends the Effort-Recovery Model by specifying its cognitive consequences: insufficient recovery not only fails to restore energetic resources but may also entrench maladaptive cognitive patterns that actively hinder rehabilitation. Sufficient recovery through detachment, relaxation, mastery, and control appears to diminish rigid cognitive schemas that can hinder adaptive work behavior and successful rehabilitation, resonating with research linking recovery to reduced cognitive rigidity[25]. From a neurocognitive perspective, adequate recovery\u0026mdash;particularly psychological detachment\u0026mdash;may facilitate this process by reducing sustained activation in the prefrontal cortex, a region critical for executive control and top-down emotion regulation[16]. This \u0026lsquo;quieting\u0026rsquo; of hypervigilant neural circuits could restore cognitive elasticity and dampen the automatic activation of ingrained, irrational beliefs[26], processes paramount for cognitive rehabilitation in occupational contexts. For organizations and rehabilitation professionals, this underscores the importance of ensuring high-quality recovery opportunities as a preventive and rehabilitative measure, such as advocating for standardized work hours, encouraging complete work disconnection, and providing recreational resources.\u003c/p\u003e\n\u003cp\u003eThe results confirmed that regulatory emotional self-efficacy partially mediated the relationship (H2). This reveals a key mechanism: recovery enhances confidence in managing emotions, a critical psychological resource during occupational stress and rehabilitation. According to social cognitive theory, successful recovery provides mastery experiences that boost regulatory emotional self-efficacy. Specifically, the \u0026lsquo;mastery\u0026rsquo; and \u0026lsquo;control\u0026rsquo; dimensions of recovery may be pivotal for fostering RESE, as they provide concrete experiences of successfully managing one\u0026rsquo;s time and activities outside of work, which can generalize to beliefs about managing internal states[8, 6]. Higher self-efficacy not only predisposes individuals to use adaptive strategies like cognitive reappraisal rather than maladaptive ones like rumination when facing negative emotions[17] but, as per the process model of emotion regulation[17], may also influence the earlier stage of \u003cem\u003eappraisal\u003c/em\u003e. Individuals with high RESE are likely to appraise work challenges as more manageable and less threatening from the outset, thereby preventing the initiation of the stress-cognition maladaptive cascade that culminates in dysfunctional attitudes[18]. This positions regulatory emotional self-efficacy as a bridge connecting external resource recovery to internal cognitive vulnerability. Interventions for surveying personnel, and within occupational rehabilitation programs more broadly, should therefore include skill-based training to enhance this self-efficacy, empowering individuals to disrupt the path from insufficient recovery to cognitive dysfunction and impaired work adjustment.\u003c/p\u003e\n\u003cp\u003eThe study also verified that anxiety sensitivity partially mediated the link (H3). Recovery benefits are partly achieved by reducing catastrophic interpretations of anxiety-related signals, which are known to exacerbate work avoidance and impede engagement in rehabilitation activities[24]. High-quality recovery, particularly deep detachment and relaxation, can lower physiological arousal and attention to anxiety symptoms, attenuating catastrophic beliefs[15]. Here, the \u0026lsquo;relaxation\u0026rsquo; dimension of recovery may be especially potent in directly countering the somatic hyper-awareness central to anxiety sensitivity[19]. Lower anxiety sensitivity implies greater tolerance for internal discomfort and reduced secondary anxiety, weakening the maintenance of irrational beliefs tied to anxiety, such as the need for absolute control[20]\u0026mdash;beliefs often observed in individuals struggling to return to demanding work. This finding bridges recovery research with transdiagnostic cognitive models, positioning anxiety sensitivity not merely as a risk factor for anxiety disorders but as a core maintenance mechanism for a broad spectrum of work-related psychological distress, including depression and burnout[20]. Thus, targeting anxiety sensitivity through recovery-oriented interventions holds significant promise for holistic occupational rehabilitation. Interventions within a rehabilitation context could include psychoeducation to normalize anxiety responses combined with guided recovery practice to directly lower anxiety sensitivity, thereby reducing a key barrier to occupational participation.\u003c/p\u003e\n\u003cp\u003eThe core finding was the confirmation of the chain mediating effect (H4): recovery experience \u0026rarr; regulatory emotional self-efficacy \u0026rarr; anxiety sensitivity \u0026rarr; dysfunctional attitudes. This aligns with the extended process model of emotion regulation[17], wherein belief in one\u0026rsquo;s regulatory ability influences the appraisal of specific emotions. Individuals with high regulatory emotional self-efficacy are more confident in managing anxiety, less likely to appraise it as dangerous, and thus exhibit lower anxiety sensitivity[12,17]. Reduced anxiety sensitivity, in turn, mitigates the cognitive rigidity associated with fear of losing control, a common theme in work-related stress and rehabilitation. This sequential pathway\u0026mdash;from external \u003cem\u003eresource\u003c/em\u003e replenishment (recovery), to internal \u003cem\u003ebelief\u003c/em\u003e empowerment (RESE), to maladaptive \u003cem\u003eappraisal\u003c/em\u003e revision (ANXS), and finally to deep-seated cognitive \u003cem\u003eschema\u003c/em\u003e change (DAS)\u0026mdash;provides a fine-grained map of how environmental support translates into lasting cognitive adaptation. It represents a significant theoretical integration, organically concatenating constructs from organizational behavior (recovery theory), social-cognitive theory (self-efficacy), clinical psychology (anxiety sensitivity), and cognitive therapy (dysfunctional schemas)[21]. This chain mechanism demonstrates that the cognitive-protective effect of recovery is realized through an orderly psychological process highly relevant to occupational rehabilitation: first enhancing emotion regulation resources (a key rehabilitation target), then altering the maladaptive cognitive appraisal of the emotional experience, and finally impacting deeper cognitive schemas that affect work behavior. This suggests that comprehensive support for high-stress workers and rehabilitation clients should be synergistic: creating conditions for recovery while prioritizing training to enhance regulatory emotional self-efficacy, subsequently lowering anxiety sensitivity to prevent dysfunctional attitudes and promote sustainable work functioning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePractical Implications for Rehabilitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present findings advocate for a multi-level, sequential intervention framework to mitigate cognitive vulnerability and promote work adaptation in high-stress, mobile occupations like surveying.\u003c/p\u003e\n\u003cp\u003eOrganizational Level: Institutionalizing Recovery Promotion. Organizations must move beyond generic employee assistance programs. For field-based personnel, this entails implementing enforceable policies such as mandatory \u0026ldquo;digital disconnection\u0026rdquo; periods after work hours, guaranteeing minimum rest periods following extended field deployments (e.g., 10-15 consecutive days), and equipping remote bases with facilities that actively foster relaxation and mastery (e.g., libraries, workshops for non-work skills)[22]. This creates the essential pre-condition for resource replenishment.\u003c/p\u003e\n\u003cp\u003eIndividual Skill Level: Targeted Emotion Regulation Capacity Building. Occupational safety and rehabilitation programs should embed evidence-based modules focused on building emotion regulation skills, such as mindfulness-based stress reduction or cognitive reappraisal training[23]. The critical aim is to facilitate \u0026ldquo;mastery experiences\u0026rdquo; in managing work-related affective states, thereby concretely elevating RESE and disrupting the link between strain and cognitive rigidity.\u003c/p\u003e\n\u003cp\u003eCognitive Restructuring Level: Normalizing and Reframing Anxiety Appraisals. Through psychoeducation, workers can learn to differentiate normative stress-induced anxiety from catastrophic fear of anxiety sensations. Cognitive-behavioral techniques can then be used to challenge catastrophic interpretations of somatic cues (e.g., \u0026ldquo;My heart is racing, so I must be losing control\u0026rdquo;) directly targeting and reducing anxiety sensitivity[24]. This final layer consolidates gains by modifying the core cognitive risk factor.\u003c/p\u003e\n\u003cp\u003eThese three tiers are synergistic: an organizational culture that legitimizes recovery provides the psychological space for skill acquisition; enhanced self-efficacy builds resilience and creates readiness for cognitive change; and modified appraisals solidify rehabilitation outcomes and prevent relapse.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and Future Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be noted. First, the cross-sectional design precludes causal inferences. Future research should employ longitudinal or experimental designs, ideally tracking individuals through periods of stress, recovery, and occupational rehabilitation. Experience sampling or diary methods would be particularly valuable for capturing the dynamic, within-person fluctuations in recovery experiences, daily regulatory self-efficacy, anxiety sensitivity, and cognitive states across typical work-rest cycles[25]. Second, the sample was drawn from a single occupational group with a significant gender imbalance, potentially limiting generalizability. Future studies should test the model in broader populations, including other high-stress occupations, and balance gender representation. Third, reliance on self-report measures introduces potential bias, despite statistical controls. Incorporating physiological indicators (e.g., heart rate variability as an index of recovery), behavioral measures of cognitive flexibility, or supervisor ratings of work adjustment would strengthen findings[26]. Finally, the model focused on intra-individual processes. Future research could incorporate contextual factors highly relevant to occupational rehabilitation, like organizational support, supervisor behavior, and team climate, to build a more comprehensive person-situation interaction model. Furthermore, future studies could explicitly link these psychological pathways to concrete occupational rehabilitation outcomes, such as return-to-work success, work engagement, or productivity, by incorporating longitudinal data from rehabilitation tracking systems.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBy testing a chain mediation model, this study systematically elucidated the psychological mechanism linking recovery experience to dysfunctional attitudes among surveying personnel, with clear implications for occupational rehabilitation. The findings confirmed that recovery experience negatively predicts dysfunctional attitudes both directly and indirectly through the independent and sequential mediating roles of regulatory emotional self-efficacy and anxiety sensitivity.\u003c/p\u003e\n\u003cp\u003eThis research makes a seminal theoretical contribution by providing the first integrative model that delineates a chain mechanism connecting organizational recovery processes with clinical cognitive vulnerability. It bridges the longstanding divide between macro-stress descriptions in occupational health and micro-cognitive mechanisms in clinical psychology, offering a coherent pathway from resource depletion to schema entrenchment. This research represents a significant interdisciplinary integration, mapping a sequential transmission pathway relevant to maintaining work ability: from external resource replenishment (recovery experience) to internal emotion regulation belief (regulatory emotional self-efficacy), to the specific cognitive appraisal of an emotional state (anxiety sensitivity), and finally to a deep-seated cognitive schema (dysfunctional attitude) that can hinder occupational adaptation. This \u0026ldquo;Resource-Belief-Appraisal-Schema\u0026rdquo; (RBAS) pathway offers a nuanced, dynamic framework for understanding the plasticity of psychological adaptation and the genesis of vulnerability in high-pressure occupations, moving beyond static correlational findings to a process-oriented explanation.\u003c/p\u003e\n\u003cp\u003ePractically, the findings compel a paradigm shift in occupational rehabilitation, from passively managing psychological symptoms to proactively constructing a robust, multi-tiered mental health promotion system centered on \u0026ldquo;resource replenishment, empowered regulation, and adaptive cognition.\u0026rdquo; It suggests an integrated, three-pronged intervention approach for occupational rehabilitation: creating and protecting recovery opportunities, empowering emotion regulation beliefs through skill training, and correcting anxiety-related catastrophic cognitions via psychoeducation. This multi-level strategy can synergistically prevent and mitigate dysfunctional attitudes at their source, thereby enhancing individual psychological resilience and promoting organizational safety, efficacy, and successful work rehabilitation. Consequently, we advocate for policy and practice that institutionalizes recovery-supportive environments, integrates emotion-regulation mastery into core competency training, and embeds cognitive-restructuring techniques into routine occupational health screenings. For occupational rehabilitation practitioners, this model provides a multi-level framework for designing targeted psychological support programs for high-stress occupational groups. Ultimately, this work calls for a systemic re-imagining of occupational rehabilitation, positioning it not merely as a remedial service but as a strategic investment in building cognitively flexible, resilient, and sustainably productive workforces.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRECX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eRecovery Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDETC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003ePsychological Detachment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRELA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eRelaxation Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eMastery Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTRL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eControl Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRESE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eRegulatory Emotional Self-Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOSF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eSE in Expressing Positive Emotion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDESM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eSE in Managing Despondency/Distress\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eANGM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eSE in Regulating Anger/Irritation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n 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121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOMP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eCompulsivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eNeed for Approval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDEPN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eDependency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUTN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eAutonomous Attitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOGP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eCognitive Philosophy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to express their sincere gratitude to the surveying and mapping personnel who participated in this study. Their willingness to share their experiences was fundamental to this research.\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the work of scholars whose foundational research in occupational rehabilitation psychology\u0026mdash;particularly concerning work ability, return-to-work processes, and the management of anxiety in occupational contexts\u0026mdash;informed the conceptual framing and practical implications of this study.\u003c/p\u003e\n\u003cp\u003eFinally, we thank our colleagues for their constructive feedback during the development of this manuscript.\u003c/p\u003e\n\u003cp\u003eSincerely,\u003c/p\u003e\n\u003cp\u003eXiang Ji\u003c/p\u003e\n\u003cp\u003eOn behalf of all co-authors\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical approval and exemption statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed by the institutional research ethics committee responsible for the oversight of social science research involving human participants. In accordance with institutional and national guidelines for minimal-risk research, the study was determined to qualify for exemption from full ethical review (waiver of formal ethics committee approval).\u003c/p\u003e\n\u003cp\u003eExempting body: Institutional research ethics committee (name and contact details available upon request).\u003c/p\u003e\n\u003cp\u003eExemption date: 02 December 2025.\u003c/p\u003e\n\u003cp\u003eExemption number/ID: Not applicable \u0026ndash; the institution does not issue numeric approval codes for exempt social science research; official exemption documentation is available upon request.\u003c/p\u003e\n\u003cp\u003eReason for exemption: The research involves only anonymous questionnaire surveys with adult participants; no intervention, invasive procedure, or experimental manipulation is conducted; no sensitive personal information is collected; all participants provide written informed consent; the study poses no more than minimal risk to participants. These characteristics meet the exemption criteria specified in the Declaration of Helsinki and the institutional policy on ethical review of human participant research.\u003c/p\u003e\n\u003cp\u003eAll procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all individual participants included in this study. Prior to the commencement of data collection, each participant was presented with a detailed information sheet outlining the study\u0026apos;s purpose, procedures, potential risks and benefits, data confidentiality measures, and their right to withdraw. Consent was explicitly obtained by the principal investigator on 02 December 2025, with signed consent forms securely archived by the corresponding author. These documents are available for review by the editors or ethics committee upon formal request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLocal Ethics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received specific approval from the local ethics review authority at the institution where the research was conducted\u0026mdash;the Institutional Review Board of University, Shandong, China. The research protocol was reviewed and approved by this local committee in accordance with its jurisdiction and operational guidelines. The study was conducted in full compliance with all applicable local regulatory and ethical requirements for human participant research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study, along with all associated materials necessary for replication, are available in the Figshare repository. This curated archive includes de-identified raw and processed data files, comprehensive documentation of the experimental procedure and variable scoring, electronic copies of the administered questionnaires, and the complete original output files from all statistical analyses (SPSS and AMOS). Access is provided via the following permanent DOI: https://doi.org/10.6084/m9.figshare.31302328. The data are publicly available under the terms of the repository\u0026apos;s access protocol. For any specific inquiries regarding the data, reasonable requests can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIoannou, L. G., Foster, J., Morris, N. B., Piil, J. F., Havenith, G., Mekjavic, I. B., \u0026amp; Flouris, A. D. (2022). Occupational heat strain in outdoor workers: A comprehensive review and meta-analysis. \u003cem\u003eTemperature, 9\u003c/em\u003e(1), 67\u0026ndash;102. https://doi.org/10.1080/23328940.2022.2030634\u003c/li\u003e\n\u003cli\u003eAlroomi, A. S., \u0026amp; Mohamed, S. (2021). The impact of job isolation on the psychological well-being and safety performance of remote workers in the oil and gas industry. \u003cem\u003eSafety Science, 133\u003c/em\u003e, 105001. https://doi.org/10.1016/j.ssci.2020.105001\u003c/li\u003e\n\u003cli\u003eKoutsimani, P., Montgomery, A., \u0026amp; Georganta, K. (2019). 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Springer.\u003c/li\u003e\n\u003cli\u003eLim, J., Teng, J., Wong, K. F., \u0026amp; Chee, M. W. L. (2016). Modulating rest-break length induces differential recruitment of automatic and controlled attentional processes upon task reengagement. \u003cem\u003eNeuroImage, 134\u003c/em\u003e, 64\u0026ndash;73. https://doi.org/10.1016/j.neuroimage.2016.03.077\u003c/li\u003e\n\u003cli\u003eGross, J. J. (1998). The emerging field of emotion regulation: An integrative review. \u003cem\u003eReview of General Psychology, 2\u003c/em\u003e(3), 271\u0026ndash;299. https://doi.org/10.1037/1089-2680.2.3.271\u003c/li\u003e\n\u003cli\u003eLazarus, R. S., \u0026amp; Folkman, S. (1984). \u003cem\u003eStress, appraisal, and coping\u003c/em\u003e. Springer Publishing Company.\u003c/li\u003e\n\u003cli\u003eManzoni, G. M., Pagnini, F., Castelnuovo, G., \u0026amp; Molinari, E. (2008). Relaxation training for anxiety: A ten-years systematic review with meta-analysis. \u003cem\u003eBMC Psychiatry, 8\u003c/em\u003e, 41. https://doi.org/10.1186/1471-244X-8-41\u003c/li\u003e\n\u003cli\u003eTaylor, S. (2014). \u003cem\u003eAnxiety sensitivity: Theory, research, and treatment of the fear of anxiety\u003c/em\u003e. Routledge.\u003c/li\u003e\n\u003cli\u003eAdkins, J. A. (1999). Promoting organizational health: The evolving practice of occupational health psychology. \u003cem\u003eProfessional Psychology: Research and Practice, 30\u003c/em\u003e(2), 129\u0026ndash;137. https://doi.org/10.1037/0735-7028.30.2.129\u003c/li\u003e\n\u003cli\u003eKnight, C., Patterson, M., \u0026amp; Dawson, J. (2019). Building work engagement through organizational resources: A longitudinal study examining the roles of person-organization fit and organizational identification. \u003cem\u003eJournal of Occupational Health Psychology, 24\u003c/em\u003e(4), 415\u0026ndash;427. https://doi.org/10.1037/ocp0000139\u003c/li\u003e\n\u003cli\u003eVonderlin, R., Biermann, M., Bohus, M., \u0026amp; Lyssenko, L. (2020). Mindfulness-based programs in the workplace: A meta-analysis of randomized controlled trials. \u003cem\u003eMindfulness, 11\u003c/em\u003e(7), 1579\u0026ndash;1598. https://doi.org/10.1007/s12671-020-01328-3\u003c/li\u003e\n\u003cli\u003eSmits, J. A. J., Berry, A. C., Tart, C. D., \u0026amp; Powers, M. B. (2008). The efficacy of cognitive-behavioral interventions for reducing anxiety sensitivity: A meta-analytic review. \u003cem\u003eBehaviour Research and Therapy, 46\u003c/em\u003e(9), 1047\u0026ndash;1054. https://doi.org/10.1016/j.brat.2008.06.010\u003c/li\u003e\n\u003cli\u003eSonnentag, S. (2001). Work, recovery activities, and individual well-being: A diary study. \u003cem\u003eJournal of Occupational Health Psychology, 6\u003c/em\u003e(3), 196\u0026ndash;210. https://doi.org/10.1037/1076-8998.6.3.196\u003c/li\u003e\n\u003cli\u003eGreiner, B. A., Krause, N., Ragland, D., \u0026amp; Fisher, J. M. (2004). Occupational stressors and hypertension: A multi-method study using observer-based job analysis and self-reports in urban transit operators. \u003cem\u003eSocial Science \u0026amp; Medicine, 59\u003c/em\u003e(5), 1081\u0026ndash;1094. https://doi.org/10.1016/j.socscimed.2003.12.006\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Recovery Experience, Dysfunctional Attitudes, Regulatory Emotional Self-Efficacy, Anxiety Sensitivity, Occupational Rehabilitation, Surveying Personnel","lastPublishedDoi":"10.21203/rs.3.rs-8749993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8749993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eSurveying and mapping personnel operate in high-demand, precision-driven environments characterized by chronic occupational stress, posing significant risks to cognitive health and work functioning. Dysfunctional attitudes—rigid, perfectionistic cognitive schemas—constitute a key vulnerability factor for depression, anxiety, and impaired occupational adaptation in this population. Prior research has primarily described stressors rather than explained how daily psychological recovery mitigates such maladaptive cognition, a gap with direct implications for occupational rehabilitation. To bridge this divide between macro-stress descriptions and micro-cognitive mechanisms, this study introduces an integrative theoretical perspective that links organizational psychology (recovery theory) with clinical cognitive vulnerability models. To address this, a chain mediation model was tested to examine whether and how recovery experience influences dysfunctional attitudes through regulatory emotional self-efficacy and anxiety sensitivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eA sample of 107 field surveying personnel completed questionnaires assessing recovery experience (REQ), dysfunctional attitudes (DAS), regulatory emotional self-efficacy (RESE), and anxiety sensitivity (ASI-3). Data were analyzed using correlation analysis and structural equation modeling (SEM) to test direct and mediating effects, with bootstrap confidence intervals calculated for indirect effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eSEM indicated that: (1) recovery experience directly and negatively predicted dysfunctional attitudes; (2) regulatory emotional self-efficacy partially mediated this relationship; (3) anxiety sensitivity also partially mediated the relationship; and (4) regulatory emotional self-efficacy and anxiety sensitivity sequentially mediated the link between recovery experience and dysfunctional attitudes via a chain pathway. Bootstrap analyses confirmed the robustness of these indirect effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThis study provides an integrative framework connecting organizational psychology (recovery theory) with clinical psychology (cognitive vulnerability models) and occupational rehabilitation, systematically revealing the mechanism from external resource replenishment to internal cognitive schema improvement. Critically, it elucidates a sequential psychological pathway—from resource recovery, through enhanced emotion-regulation belief, to reduced catastrophic anxiety appraisal, and finally to attenuated dysfunctional cognition—that offers novel multi-level intervention targets for occupational rehabilitation. The findings highlight that interventions aimed at enhancing recovery quality, fostering emotional regulation beliefs, and correcting catastrophic interpretations of anxiety can help prevent and alleviate dysfunctional attitudes, thereby boosting individual psychological resilience and occupational functioning. These findings suggest integrated interventions for occupational rehabilitation programs targeting recovery, emotion regulation, and anxiety sensitivity.\u003c/p\u003e","manuscriptTitle":"Discussion Enhancing Occupational Rehabilitation in High-Stress Work: How Recovery Experience Reduces Dysfunctional Attitudes Through Emotional Regulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 11:19:27","doi":"10.21203/rs.3.rs-8749993/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-06T12:30:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-27T06:43:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-12T01:55:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-02-12T01:52:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"dc4c6be1-a4c4-4f58-b7cc-741832fb1a47","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":64131349,"name":"Health sciences/Health care"},{"id":64131350,"name":"Biological sciences/Psychology"},{"id":64131351,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-11T11:19:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 11:19:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8749993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8749993","identity":"rs-8749993","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}