Topic Modeling the Academic Discourse on Critical Incident Stress Debriefing and Management (CISD/M) for First Responders

Abstract

Background/Objectives: This study examines the academic discourse surrounding Critical Incident Stress Debriefing (CISD) and Critical Incident Stress Management (CISM) for first responders using Latent Dirichlet Allocation (LDA) topic modeling. It aims to uncover latent topical structures in the literature and critically evaluate assumptions to identify gaps and limitations. Methods: A corpus of 214 research article abstracts related to CISD/M was gathered from the Web of Science Core Collection. After preprocessing, we used Orange Data Mining software’s LDA tool to analyze the corpus. We tested models ranging from 2 to 10 topics. To guide interpretation and labeling, we evaluated them using log perplexity, topic coherence, and LDAvis visualizations. A four-topic model offered the best balance of detail and interpretability. Results: Four topics emerged: (1) Critical Incident Stress Management in medical and emergency settings, (2) psychological and group-based interventions for PTSD and trauma, (3) peer support and experiences of emergency and military personnel, and (4) mental health interventions for first responders. Key gaps included limited focus on cumulative trauma, insufficient longitudinal research, and variability in procedural adherence affecting outcomes. Conclusions: The findings highlight the need for CISD/M protocols to move beyond event-specific interventions and address cumulative stressors. Recommendations include incorporating holistic, proactive mental health strategies and conducting longitudinal studies to evaluate long-term effectiveness. These insights can help refine CISD/M approaches and enhance their impact on first responders working in high-stress environments.

1. Introduction

As part of their occupational duties, first responders, including military personnel, police officers, firefighters, and medical professionals, inevitably encounter violent and traumatic events that meet diagnostic-specific incident criteria for Post-Traumatic Stress Disorder (PTSD) [1], an anxiety disorder known to significantly affect performance, mental health, and retention [2]. First responders frequently encounter traumatic incidents as part of their routine duties, including mass casualty events, violent accidents, and unpredictable interactions with citizens. To address this, various interventions have been developed to support first responders post-incident, among which Critical Incident Stress Debriefing and Critical Incident Stress Management (CISD/M) are most prominent [3].

1.1. Rationale

The purpose of this study is twofold. First, it analyzes the latent topical structures in the academic literature on CISD/M to uncover implicit assumptions shaping the field. To accomplish this, we employ topic modeling through Latent Dirichlet Allocation (LDA), a computational technique that systematically identifies thematic patterns in text collections. LDA is an unsupervised machine learning algorithm that discovers hidden topics by analyzing how words are distributed across documents [4]. Unlike manual content analysis, which may be influenced by researcher expectations, LDA identifies topics mathematically by examining word frequency and co-occurrence patterns (collocations) within a text corpus [5]. The LDA approach is particularly valuable for studying CISD/M literature for it can conduct the following:

• Provide a data-driven method to distill complex, multifaceted discussions into interpretable topics, reducing potential researcher bias.
• Process large volumes of text that would be impractical to analyze manually, allowing for a broader examination of the entire field.
• Facilitate the discovery of implicit connections between concepts that might not be apparent through traditional literature review.

This methodological approach enables us to map the breadth of academic discourse, pinpoint recurring themes, and expose potentially underexplored areas that may influence the effectiveness of these interventions for first responders. Studies in related fields have successfully utilized LDA to uncover meaningful topical structures such Psychological First Aid training manuals and on burnout among first responders, demonstrating its efficacy in analyzing mental health and trauma-related discourse [6,7].

Second, inspired by Alvesson and Sandberg’s problematization perspective on research rationale statements [8], we intend to use the results from our topic modeling study to begin to critically question taken-for-granted assumptions in CISD/M research. Problematizing the literature in this way brings critical gaps and limitations to light, broadening the conversation and steering CISD/M scholarship toward new directions.

1.2. Background

To inform our selection of variables and methodologies, we explored the academic literature across several key areas. Specifically, we examined (1) existing knowledge of Critical Incident Stress Debriefing (CISD), (2) insights into Critical Incident Stress Management (CISM), and (3) the topical structure of academic discourse on CISD/M. Following this exploration, we detailed the research question guiding our study.

1.2.1. CISD

The academic discourse on Critical Incident Stress Debriefing (CISD) reflects diverse and often conflicting perspectives regarding its implementation and effectiveness [9,10]. CISD is a structured, multiphase group intervention facilitated by trained personnel shortly after a traumatic event, with the goal of mitigating psychological distress and preventing long-term harm [11]. As one of the more formalized components of the broader Critical Incident Stress Management (CISM) framework, CISD is frequently regarded as its most visible and widely applied element [11].

1.2.2. CISM

While CISM is a widely recognized and accepted intervention, much of the existing literature shows a distinct focus on micro-evaluation or the evaluation of CISM in the context of a specific critical incident at the expense of evaluating the universality and effectiveness of the model itself. Critical Incident Stress Management (CISM) is characterized as a crisis intervention system, which is comprehensive in its inclusion of seven integrative components designed to mitigate the psychological impacts and provide targeted support to first responders who have experienced traumatic incidents [12]. Thus, CISD/M are widely used by first responders, such as police, firefighters, and EMS personnel, because they believe these interventions effectively help manage stress from traumatic experiences [11,12,13].

1.2.3. CISD/M

Previous reviews of the academic literature targeting CISD/M have established a comparatively limited amount of information about the efficacy of CISD/M over time, continued burnout of first responders despite the implementation of CISD/M, and the integration of more current and innovative discoveries in the fields of mental health science, stress, and holistic resiliency. Stileman and Jones’ meta-analysis provides a clear picture of the uneven outcomes and methodological challenges occurring in the area of study [14].

1.2.4. CISD/M and Topic Modeling

Although the topical structure of CISD/M academic discourse remains underexplored, studies in related fields provide valuable insights. For example, two studies have utilized LDA in closely related fields, shedding light on their topical structures. One study focused on the topic structure of Psychological First Aid training manuals [6], showing a breadth of topics covered in the manuals and consistency across manuals across time. Another study focused on the topic structure of burnout in the research literature on first responders showing that there were a variety of topics present, while also identifying major gaps in the literature, specifically a focus on the prevention of burnout and cultural nuances related to burnout [7].

1.3. Research Question

Given the aforementioned, one research question (RQ) was designed to guide the present study. The question is as follows:

RQ1: 

What is the topical structure of the academic discourse on Critical Incident Stress Debriefing and Critical Incident Stress Management?

2. Methods

2.1. Design

This study employed Latent Dirichlet Allocation (LDA) for topic modeling. LDA is an unsupervised machine learning technique that identifies latent topics by analyzing word frequency and co-occurrence patterns (collocations) within a text corpus [4,5]. This methodology was selected given its proven utility in identifying trends and gaps within the academic literature [15]. LDA groups words into topics based on their distribution across documents and their probability of occurring within a specific topic. The unit of analysis is the individual word (i.e., token), with continuous measurements reflecting the probability of a word belonging to a given topic. This study utilized Orange Data Mining software (3.37, University of Ljubljana, Ljubljana, Slovenia), which implements LDA through the Gensim Python library [16]. The optimal number of topics was determined by comparing models with varying numbers of topics ($k$) and selecting the model that best balanced two complementary evaluation metrics: log perplexity and topic coherence. Log perplexity measures how well a model predicts held-out data, with lower values indicating better fit. Topic coherence assesses the semantic similarity of top words within each topic, with higher values indicating more interpretable topics. By minimizing perplexity while maximizing coherence, we identified a model that offers both statistical validity and practical interpretability. These metrics are described in greater detail in Section 2.3. Given the public and published nature of the data analyzed, no human subjects review was required.

2.2. Corpus

The CISD/M corpus consists of abstracts from research articles. The use of abstracts as an efficient and effective proxy for the full text of academic journal articles in topic modeling research is well established [17,18,19]. The abstracts were scraped from the Web of Science Core Collection (WoS) on 19 March 2024 [20]. The parameters used for the search included the following:

• Document types: articles, review articles, letters, meeting abstracts, early access, notes, and proceeding papers.
• Language: English.

The exact code of the WoS search was as follows:

“Critical Incident Stress” (All Fields) AND Critical Incident Stress (OR—Search within topic) AND Critical Incident Stress Debriefing (OR—Search within topic) AND Critical Incident Stress Management (OR—Search within topic) AND Psychological Debriefing (OR—Search within topic) AND Critical Incident Stress Management CISM (OR—Search within topic) AND Critical Incident Stress Debriefing CISD (OR—Search within topic) AND CISM (OR—Search within topic) AND CISD (OR—Search within topic).

We included a variety of document types to capture diverse scholarly communication and discussions related to CISD/M, providing a comprehensive view of the field. The search term “CISD” yielded a number of irrelevant results, as the initialism is also used in other fields (e.g., CISD protein, cat iris sphincter smooth muscle, and Center for Integrated Space Weather Modeling). To address this, we employed keyword-based exclusion filters, removing articles containing terms such as “space,” “muscle,” and “protein.” This initial screening was conducted collaboratively by the 2nd, 4th, and 6th authors, with any ambiguous cases discussed and resolved through consensus. Articles lacking abstracts were also excluded. Following this, all remaining abstracts were manually reviewed by the authors to confirm their relevance to CISD/M activities involving or affecting first responders. Given the clearly defined and concrete exclusion criteria, we did not compute formal inter-rater reliability metrics. In our judgment, the clarity of the criteria combined with collaborative review and consensus minimized the potential for disagreement or misclassification. This process resulted in a final corpus of 214 abstracts.

Each abstract was then converted into a separate .txt file. Standard preprocessing steps, including, e.g., stop word removal and bag-of-words techniques, were employed to prepare the corpus for analysis. The final corpus contained 41,035 words. The range of words for the abstracts was from 51 to 525 with a mean of 191.75 (SD = 80.58, Skewness = 0.90), a median of 182, and a mode of 138. The complete abstract files, a detailed account of the preprocessing steps, the Python code used for corpus analysis, and additional visualizations are all available on the project’s website (https://osf.io/edvrz/).

2.3. Measures

2.3.1. Topic Modeling

The LDA approach to topic modeling identifies underlying topic structures and hidden variables from the observable variables, such as specific words and word frequencies. LDA is a reliable tool for identifying hidden variables [4]. It is helpful to think of a topic or the hidden variable as a cluster of words grouped together based on the specific LDA process.

2.3.2. $k$

For an LDA analysis, the term $k$ refers to the number of topics that are generated. Technically, any number of topics can be generated from a corpus. As noted earlier, the goal of the LDA is to have the lowest perplexity while having the highest coherence. Thus, the process of identifying $k$ is to run the LDA based on a certain number of topics (say 2 to start with), and then scale up one topic at a time until the perplexity begins to trend up and coherence begins to go down.

2.3.3. Log Perplexity

Perplexity is an evaluation metric in LDA research. Specifically, it is a measure of how accurately the model predicts or interprets new data. It uses a log-likelihood in the computation [21], and a lower score indicates a better topic due to the words within each topic being more homogeneous to the topic involved [22].

2.3.4. Topic Coherence

Coherence is an evaluation metric in LDA research. This metric is the measure of the degree to which words within a topic are interconnected and related [21]. A higher coherence indicates topics that are more semantically and statistically related, while a lower coherence indicates the opposite. Thus, a higher coherence score is desired.

2.3.5. Word Relevance

A word’s relevance is defined by a weighted combination of (1) how frequently a term appears within a specific topic and (2) how exclusive that term is to the topic compared to its overall usage across all documents [23]. When calculating word relevance, a weighting parameter (λ) is used to adjust the balance between term frequency and term exclusivity [23].

2.4. Apparatus

2.4.1. Orange Data Mining Widgets

Data preprocessing and analysis were conducted using Orange Data Mining software, an open-source, Python-based platform designed for data mining and machine learning workflows [24]. Topic modeling was executed through Orange’s Topic Modeling widget, which implements Latent Dirichlet Allocation (LDA) using the Gensim Python library [16]. This widget enables users to define the number of topics (denoted as k), a key parameter controlling the granularity of the model. Gensim internally manages other key hyperparameters in the LDA algorithm. These include the document-topic prior (alpha), topic-word prior (eta), number of iterations, number of passes over the corpus, and the random seed. Default settings in Orange configure these as follows: a symmetric alpha and eta (automatically inferred unless specified), 50 iterations, a single pass through the data, and an unspecified random seed (random_state = None) [16,25]. These settings ensure computational efficiency while maintaining model stability. To determine the optimal number of topics (k), we compared multiple models using two complementary evaluation metrics: log perplexity and topic coherence. We selected the model that struck the best balance between statistical fit and interpretability.

Visualization of the topic models was performed using Orange’s LDAvis widget [26], which integrates the LDAvis framework developed by Sievert and Shirley [23]. This tool provides interactive, web-based visualizations to aid in topic interpretation. Specifically, it generates overlaid bar charts that juxtapose each word’s overall frequency across the corpus with its frequency within a selected topic, offering insight into word relevance. The widget further outputs the 20 most relevant words per topic, where relevance is calculated as a weighted combination of term frequency and exclusivity to the topic, with the relevance parameter (λ) set to 0.6 [23]. These visualizations supported the systematic interpretation and differentiation of topics derived from the CISD/M abstracts.

2.4.2. Use of Generative AI

Consistent with the MDPI [27] and WAME [28] ethical guidelines for documenting generative AI use, we employed generative AI tools (specifically ChatGPT-4o, Gemini 1.5, and Claude 3 Sonnet) at multiple points during this research. We specifically used these tools for iterative brainstorming of plausible explanations for observed results and possible responses to reviewers, drafting and revising sections of the manuscript (abstract, apparatus, results, and discussion), labeling topics derived from results, and preparing responses to reviewer comments. All data collection, statistical analysis, interpretation, and inference were performed using conventional software without AI assistance. Throughout, we maintained close oversight of all AI interactions and remain fully responsible for the originality, accuracy, and integrity of the content presented here. No GenAI tool is listed as an author, and the human authors assume full responsibility for the originality, validity, and integrity of the manuscript. Consistent with MDPI requirements, the use of AI—including tool identity, prompt inputs, and outputs—is transparently declared and documented herein, with full logs of GenAI activities available on the project’s website (https://osf.io/edvrz/).

2.5. Data Analysis

The data analysis plan contains seven steps. First, topic models ranging from $k$ = 2 to $k$ = 10 are produced to explore different levels of thematic granularity. Second, the optimal number of topics ($k$) is determined based on a combination of criteria, including low log perplexity, high topic coherence, and meaningful levels of detail. Third, LDAvis visualizations are used to interpret and differentiate between topics, leveraging the preset relevance parameter (λ = 0.6) for balanced interpretation. The complete Orange workflow file is presented in Figure 1. Fourth, the results from the Topic Modeling and LDAvis widgets are submitted to generative AI models to create preliminary labels for each topic. These models have been shown to be effective for topic labeling [29]. The specific approach used was multi-persona prompting [30]. The text of the prompt is available on the project’s website: https://osf.io/edvrz/. Fifth, the AI-generated labels are revised to ensure accuracy and clarity in light of the context of CISD/M discourse. Sixth, the fourth and fifth steps are repeated in an iterative manner to identify the topic structure most representative of the academic discourse on CISD/M. Seventh, logs documenting the use of generative AI, additional visualizations, and supplementary materials are made available for readers to inspect on the project’s website: https://osf.io/edvrz/.

3. Results

3.1. Model Solution

Coherence and log perplexity scores were determined for $k$ solutions from 2 to 10. Coherence and log perplexity scores for all solutions can be reviewed on this project’s website: (https://osf.io/edvrz/). While the three-topic solution showed slightly better log perplexity (28.46495 vs. 28.56290) and a modest edge in topic coherence (0.43741 vs. 0.40217), we ultimately selected the four-topic solution based on its greater interpretive utility. In particular, the four-topic model provided clearer topical boundaries and reduced keyword redundancy across topics—an important consideration given the overlapping terminology common in CISD/M discourse.

3.1.1. Three $k$ Solution

In the three-topic solution, several high-probability keywords (e.g., intervention, debriefing, incident, and CISD) appeared repeatedly across multiple topics, making it difficult to delineate between distinct subdomains. For example, “intervention” and “CISD” appeared in both Topic 1 and Topic 2, while “incident” and “support” spanned across all three. This level of lexical overlap suggested that the three-topic model was blending conceptually different areas—particularly general first responder support and military-specific peer response—into single clusters.

3.1.2. Four $k$ Solution

By contrast, the four-topic model showed greater thematic clarity and less overlap among key terms. It cleanly separated military peer response programs (Topic 3) from general mental health interventions for first responders (Topic 4), which the three-topic solution had conflated into a single topic. Similarly, it drew a clearer boundary between structured debriefing processes (Topic 2) and field implementation practices (Topic 1), allowing us to isolate discourse around policy from that around outcomes.

This improvement in semantic distinctiveness made the four-topic model better suited for our goal of uncovering latent structures and assumptions in the CISD/M literature. While the differences in statistical metrics were minor, the added granularity of the four-topic solution allowed us to generate more focused interpretations and surface more nuanced gaps in the literature.

3.2. Description of Each Topic

The LDA model surfaced four topic clusters that map cleanly onto known domains of CISD and trauma-informed peer response. Labels were assigned based on keyword frequency, LDAvis relevance, and how well each cluster aligned with established practices in emergency response, military peer support, and post-incident mental health care. LDAvis visualizations for each topic are available on the project’s website (https://osf.io/edvrz).

3.2.1. Topic 1—Emergency Staff CISD Implementation Practices

Keywords were incident, critical, CISD, CISM, use, debriefing, medical, management, staff, and emergency. This topic reflects how CISD is carried out in emergency medical settings. The terms point to structured protocols, not just informal support. “Medical,” “staff,” and “incident” ground it in frontline care; “process” and “use” suggest operational rollout. The label fits both the population and the intervention focus.

3.2.2. Topic 2—Group Psychological Debriefing Outcome Studies

Keywords produced included debriefing, intervention, group, PTSD, psychological, symptom, post, CISD, effect, and traumatic. This topic centers on outcome evaluations of group-based debriefings. The focus is clearly on empirical terms like effect, participant, and studies, which are direct markers of research. Combined with PTSD and psychological, the topic points to the long-standing question of how well CISD mitigates trauma symptoms. The label reflects both the format and the outcome-driven framing.

3.2.3. Topic 3—Military Peer Response Support Programs

Keywords noted were emergency, military, personnel, incident, response, peer, experience, support, service, and critical. This topic tracks peer-based interventions in military contexts. Peer, support, and model point to structured systems, not informal systems. Military, personnel, and incident make the setting clear. The label reflects how peer teams are organized and deployed post-incident within military and tactical units.

3.2.4. Topic 4—First Responder Mental Health Interventions

Keywords encountered were mental, health, support, first, service, intervention, PTSD, exposure, traumatic, and program. This topic centers on post-trauma mental health care for first responders. PTSD, exposure, and traumatic define the risk; support, intervention, and program point to organized efforts to address it. The terms do not point to a single model, but the theme is clear, namely structured mental health interventions targeting frontline responders. The label covers the scope without overreaching.

With the topic structure of the CISD/M literature now well defined, we can turn our attention to what the obtained results mean.

4. Discussion

The primary objective of this study was a preliminary exploration of the underlying topical structure of the academic literature on CISD/M. Our discussion will begin by examining all four topics identified through our LDA analysis, considering possible explanations for their emergence and prominence in the literature. Then, we will address the methodological limitations of this exploratory study. Finally, we will outline implications for future research directions, including potentially underexplored areas that were not prominently represented in our topic modeling results. While definitive practice recommendations would be premature at this exploratory stage, we will identify research questions that could ultimately inform evidence-based practice.

4.1. Reasons for the Obtained Topics

4.1.1. Emergency Staff CISD Implementation Practices

This topic reflects how CISD/M are implemented in emergency and medical contexts, with an emphasis on structured, staff-based protocols following acute critical incidents. These incidents, such as mass casualty events, violent assaults, and severe accidents, significantly impact healthcare providers. These events can trigger acute stress reactions that, if left unmanaged, may lead to long-term psychological consequences [31]. The cumulative exposure to traumatic events increases the risk of mental health disorders and contributes to burnout, characterized by emotional exhaustion, depersonalization, and a diminished sense of personal accomplishment [32,33,34]. Burnout among healthcare providers is linked to reduced job performance, higher rates of medical errors, and lower quality of patient care [35].

Healthcare workers facing high levels of stress are also at greater risk of developing mental health conditions, including depression, anxiety, and PTSD [36]. To address these challenges, CISD/M have been widely adopted to provide immediate support following traumatic events [3]. These interventions aim to facilitate emotional processing, reduce acute stress reactions, and prevent long-term psychological harm. By mitigating these adverse effects, such programs can enhance mental health and support resilience in high-pressure medical and emergency settings [37].

4.1.2. Group Psychological Debriefing Outcome Studies

This topic centers on empirical studies evaluating the outcomes of group-based psychological debriefing, particularly in relation to PTSD and trauma recovery. Psychological debriefing helps individuals cope with the aftermath of trauma by addressing emotional and psychological needs immediately after a traumatic event. These interventions aim to mitigate trauma’s impact, reduce stress, and promote resilience [38]. Typically conducted in group settings by trained professionals, debriefing sessions provide participants with an opportunity to share experiences and reactions [3]. The primary goal of psychological debriefing is to assist individuals in processing their emotions and experiences to facilitate recovery and alleviate stress [39].

Research demonstrates that well-structured psychological debriefing can significantly reduce acute stress symptoms and enhance long-term psychological well-being. However, findings from Topic 2, “Psychological and Group-Based Interventions for PTSD and Trauma,” highlight critical factors that influence its success, including the timing and quality of the debriefing process [40]. Studies show that participants in well-facilitated sessions report lower levels of stress and anxiety compared to those who do not receive such interventions [41]. These results emphasize the importance of structured debriefing as a foundational element of trauma recovery. They also reveal a gap in understanding the conditions that improve its effectiveness such as the ideal timing for implementation and the expertise required of facilitators. Addressing these gaps is essential for maximizing the impact of psychological and group-based interventions on PTSD and trauma outcomes.

4.1.3. Military Peer Response Support Programs

This topic captures trauma response frameworks in military and tactical settings, especially peer support models tailored to shared stress environments. The results for this topic emphasize the critical importance of understanding behavioral responses to trauma in developing effective support systems for first responders. Behavioral responses to critical incidents are shaped by factors such as individual resilience, the availability of social support networks, and prior exposure to trauma [42]. Recognizing these influences allows mental health professionals to design interventions that address the unique needs of first responders. Peer support programs, for instance, provide a communal space where individuals can share experiences, validate emotions, and reduce feelings of isolation. This approach is particularly valuable for emergency and military personnel who often face shared stressors in high-pressure environments.

The analysis also highlights the significance of community outreach initiatives and adaptive coping strategies in fostering resilience. Strengthening social support networks and addressing context-specific risks can help mitigate adverse psychological outcomes such as PTSD and burnout [43,44]. These findings underscore that peer support is a fundamental component of effective trauma management, particularly in high-stress professions like policing, firefighting, and military service.

4.1.4. First Responder Mental Health Interventions

This topic focuses on formal clinical mental health interventions, such as CBT and exposure therapy, used to support recovery and resilience in first responders. Integrating clinical therapy approaches into treatment plans is essential for achieving positive outcomes for first responders affected by trauma. These plans often incorporate a combination of therapies, such as Cognitive Behavioral Therapy (CBT) and exposure therapy, tailored to meet the specific needs of individuals. CBT focuses on helping patients reframe negative thoughts and develop healthier coping mechanisms, while exposure therapy involves gradual, controlled exposure to trauma-related stimuli to reduce fear and anxiety [45]. The primary objective of these therapies is to alleviate the psychological impact of traumatic events and support long-term recovery.

By addressing both cognitive and emotional responses to trauma, these interventions help first responders build resilience and reduce the risk of chronic mental health issues, such as PTSD and depression [46]. Effective treatment plans also include continuous assessment and adjustment based on individual progress and feedback. This iterative process ensures that interventions remain relevant and effective, supporting sustained recovery and resilience [47]. By adopting a holistic approach to treatment, these plans address the multifaceted nature of trauma, considering mental, emotional, and physical well-being. This topic underscores the critical role of clinical therapy approaches in supporting first responders’ mental health and facilitating recovery from trauma.

4.2. Limitations

First, the Web of Science (WoS) search parameters used to construct the corpus were based on our clinical and research experience with CISD/M. While these judgments were informed, they may have excluded relevant concepts outside our expertise. Additionally, the lack of more qualitative triangulation methods could have perhaps limited a full understanding of the topics uncovered. Future studies could benefit from consulting a broader range of CISD/M professionals, incorporating additional databases, and employing qualitative methods to ensure comprehensive corpus formation and topic interpretation. Second, while the use of abstracts when topic modeling the academic literature is widely used and accepted, nuances and subthemes may have been missed in this approach. Third, the generative AI tools used to label the topics work as a “black box”, making potential biases in labeling difficult to assess. Fourth, the Latent Dirichlet Allocation (LDA) output is highly dependent on the hyperparameter settings chosen during analysis [48]. With the exception of the number of topics hyperparameter, Orange does not permit hyperparameter selection beyond Gensim’s defaults. This limits the ability to tune the model for our specific corpus. Without the flexibility to adjust these parameters, we cannot rule out the possibility that alternate configurations might have produced more coherent or semantically distinct topics. In short, while the tool offers accessibility and ease of use, its fixed settings may constrain the precision and nuance of the topic structure, especially in smaller or more heterogeneous corpora like ours.

4.3. Implications

4.3.1. Research Implications

Occupational Stress and Burnout

Our topic modeling analysis revealed a focus on acute critical incidents within medical and emergency settings (Topic 1), reflecting an implicit assumption that CISD/M programs effectively address the unique stressors inherent to first responder roles through standard interventions. However, this thematic focus overlooks the distinct and cumulative impacts of vicarious traumas and repeated exposure to stressors, both of which contribute significantly to burnout and more severe conditions such as PTSD. Using Alvesson and Sandberg’s [8] problematization methodology, we identified a crucial gap: the limited integration of compounded trauma into current CISD/M protocols. This gap restricts the development of interventions that fully address the complexities of first responder experiences, including how pre-existing traumas influence their participation and outcomes in these programs. Tailoring CISD/M programs to address cumulative and ongoing stressors may improve their ability to reduce burnout and support mental health. Expanding the scope of these interventions to reflect the layered nature of trauma could make them more effective for high-stress professions.

Need for Longitudinal Studies

The topic modeling results revealed a strong focus on immediate psychological debriefing and group-based interventions for PTSD and trauma (Topic 2), suggesting an implicit assumption that short-term interventions are sufficient to address long-term mental health outcomes. Our critical analysis uncovered a notable gap in the literature: the lack of longitudinal studies evaluating the sustained efficacy of CISD/M interventions. This gap limits our understanding of their long-term impact and leaves unanswered questions about their effectiveness in promoting enduring mental health among first responders. One example of a future study could examine whether repeated use of CISD/M leads to ongoing psychological benefits or shows signs of diminishing impact over time. Our findings underscore the need for further research that rigorously examines CISD/M processes and outcomes over extended periods. Longitudinal studies would provide valuable insights into the long-term benefits and potential limitations of these interventions, helping to refine and adapt them as models for mitigating trauma in high-stress professions.

Analysis of Procedural Adherence and Potential for Harmful Effects

Our analysis of the literature revealed a thematic focus on the implementation of CISD/M programs without a critical examination of procedural variability and its impact on outcomes (Topic 3). We believe that this may reflect an implicit assumption that the flexibility of CISD/M protocols, allowing customization based on resources and personnel, inherently leads to effective trauma management. However, our problematization approach identified that such procedural variability can result in substantially divergent outcomes, potentially leading to iatrogenic effects. Examples of this are questions such as the following addressing CISD timing:

• What is the optimal window for administering CISD?
• Does strict or flexible adherence to a CISD’s protocol on when to administer such an intervention lead to better outcomes?

For example, one could conduct a formative evaluation to assess procedural adherence across multiple agencies (police, fire, and EMS) to uncover setting-specific barriers to fidelity. Our findings highlight the importance of understanding the effects of procedural variations to improve the consistency and effectiveness of CISD/M programs. Addressing these gaps would help researchers and practitioners refine intervention strategies and ensure that first responders receive trauma support tailored to their needs while minimizing the risk of unintended negative outcomes.

Toward More Replicable and Tunable LDA Workflows

Orange’s implementation of Latent Dirichlet Allocation (LDA) offers an accessible and intuitive entry point for topic modeling, which is particularly well suited to the exploratory analysis of academic discourse. However, its functionality is limited when it comes to hyperparameter tuning. Users can adjust only the number of topics ($k$), while other key settings, such as the document-topic prior (α), topic-word prior (β), number of iterations, and convergence thresholds, are fixed to Gensim’s defaults. Notably, the inability to set a random seed within Orange limits the replicability of results, making it difficult to reproduce or compare model outputs across sessions. In addition, hyperparameters such as iterations and passes play an important role in model stability. Increasing these values allows the model to better refine topic distributions and reduces variability in topic assignments across runs. Orange does not expose these settings, which may limit the stability of resulting topic structures, particularly in smaller or more heterogeneous corpora. Overall, these hyperparameter constraints may narrow the scope and reliability of findings, especially in studies that require greater methodological precision. Future work might benefit from the use of analysis platforms that support full hyperparameter tuning.

4.3.2. Implications for Practice

Reactive Application

The predominance of reactive, event-specific interventions in the literature (Topics 1 and 2) reflects an implicit assumption that trauma arises primarily from single, acute incidents. Our critical analysis challenges this assumption, emphasizing the need to expand the scope of CISD/M protocols to address cumulative stressors and align with contemporary understandings of stress and trauma responses. Existing protocols should be broadened to include proactive interventions that address common sources of first responder stress, such as organizational tensions, vicarious trauma, repeated micro-stressors, and other resilience factors. By incorporating these elements, CISD/M interventions can more effectively support resilience and holistic wellness in trauma management by moving beyond the limitations of reactive, event-specific approaches.

Organizational Investment

Our findings highlight that organizational support extending beyond immediate critical incidents is essential for mitigating stress and reducing burnout among first responders. The assumption that implementing CISD/M protocols alone is sufficient overlooks the importance of broader organizational initiatives. Promoting mental well-being requires policies that support work–life balance, provide access to mental health resources, and foster a supportive work environment [49]. Furthermore, organizations must actively work to reduce the stigma surrounding mental health support to encourage the use of available resources [50]. By addressing these systemic factors, organizations can create an environment that improves mental well-being and supports effective risk management strategies. This approach is critical for reducing burnout and improving the overall resilience and well-being of first responders.

Comprehensive Approaches

Our topic modeling analysis identified a lack of evolution in CISD/M interventions to incorporate broader approaches to stress and trauma management (Topic 4). This suggests an implicit assumption that traditional protocols are sufficient for addressing the psychological impacts of trauma. However, current research indicates that effective trauma response should be part of a holistic resilience framework encompassing multiple dimensions of wellness, including physical, emotional, spiritual, and financial well-being. Integrating traditional CISD/M protocols with these broader approaches could better support resilience and promote holistic wellness in trauma management. Continuous evaluation and monitoring of program outcomes are essential for assessing effectiveness, identifying areas for improvement, and ensuring efficient resource use. Evaluation methods such as surveys, interviews, and observational studies provide clarity into participant experiences and the impacts of these programs on mental health and performance [51]. For example, a qualitative study of critical incident debrief training illustrated how comprehensive evaluation can inform program enhancements [52]. By adopting more holistic approaches, organizations can improve CISD/M outcomes and better address the multifaceted needs of first responders.

5. Conclusions

The application of CISD/M is inherently unique and incident-specific, making comparative studies difficult without substantial data harmonization. The sudden, unpredictable, and chaotic nature of critical incidents [43] further complicates systematic study and evaluation. This study highlights the critical need for longitudinal research and detailed data analysis to better understand the patterns, risk factors, and effectiveness of interventions addressing behavioral responses to trauma among first responders. Additionally, our findings point to the importance of adopting a more dynamic and integrated approach to trauma management. Such an approach should include continuous evaluation, support for holistic wellness, and strategies that address both acute and cumulative stressors. By embracing these elements, trauma management frameworks can more effectively meet the complex and evolving needs of first responders.