Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time‐varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross‐sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

Objectives: For years, general practitioners (GP) shortage and patients’ increasing demand for acute care have been associated with Emergency Department (ED) crowding. Indeed, EDs admissions for non-emergency care seem to constantly increase. Surprisingly, the rationale for patients own decision to directly reach EDs over primary care have been poorly investigated to date. Methods: We conducted a study on patients admitted in two University EDs during nine consecutive days. Patients were asked to answer a survey about their frames for coming and if they were self-referred, referred by a GP, a specialist or after calling the Emergency Number. Results: During the study period, 68.0% of patients were self-referred, 17.0% referred by their GP, 8.5% by a specialist and 7% after an emergency call. 51.0% of the self-referrals thought EDs were the appropriate location to deal with their health problem and 24.0% because of a better accessibility. We noticed that 15.0% of the incomings looked for specialized care and 4.22% reported that the stress had motivated them. Of note, 4.6% of the patients were attracted by the hospital reputation. Financial concerns represented less than 1.0% of the motives invocated. Conclusion: We found that patients’ self-perceived severity of illness is the predominant frame to each the ED when they face needs for acute care. EDs’ accessibility as compared with other facilities also seems to encourage patients to come to the ED. Other factors such as the hospital reputation or patients’ stress tend to influence ED attendance but to a much lesser extent.

Background: Advances in therapies have significantly improved the outcomes of patients with cancer. However, multidimensional symptoms negatively impact patients’ quality of life. Traditional symptom analysis methods fail to capture the dynamic and interactive nature of these symptoms, limiting progress in supportive care. Network analysis (NA) is a promising method to evaluate complex medical situations. Objective: We performed a systematic review to explore NA’s contribution to understanding the complexity of symptom experiences in patients with cancer. Methods: The research question was as follows: “In patients with cancer (population), what is the contribution of NA (intervention) to understanding the complexity of multidimensional symptom experiences (outcome)?” The keywords “network analysis” AND “symptoms” AND “cancer survivors” OR “cancer patients” were searched in MEDLINE, Embase, Google Scholar, and Scopus between 2010 and 2024. Citations were extracted using Covidence software. Two reviewers independently screened the articles and resolved inclusion disagreements through consensus. Data were synthetized, and results have been narratively described. Bias analysis was performed using the Methodological Index for Non-Randomized Studies tool. Results: Among 764 articles initially identified, 22 were included. Studies evaluated mixed solid tumors (n=10), digestive tract cancers (n=4), breast cancer (n=3), head and neck cancer (n=2), gliomas (n=2), and mixed solid and hematological cancers (n=1). Twelve studies used general symptom assessment tools, whereas 10 focused on neuropsychological symptoms. Moreover, 1 study evaluated symptoms at diagnosis, 1 evaluated them during curative radiotherapy, 4 evaluated them during the perioperative period, 5 evaluated them during chemotherapy, 4 evaluated them during ongoing cancer therapies, and 7 evaluated them after acute treatments. Among these, 3 evaluated the longitudinal changes in symptom networks across chemotherapy cycles, and 1 evaluated changes during radiotherapy. Three studies investigated the associations between symptoms and biological parameters. Several NA approaches were used: network visualization (n=1), Bayesian network (n=1), pairwise Markov random field and IsingFit method (n=1), unregularized Gaussian graphical model (n=2), regularized partial correlation network (n=6), network visualization and community NA (n=1), network visualization and Walktrap algorithm (n=1), undirected network model with the Fruchterman-Reingold and edge-betweenness approaches (n=4), biased correlation and concise pattern diagram (n=1), extended Bayesian information criterion graphical LASSO method (n=3), cross-lagged panel network (n=1), and unspecified NA (n=3). Psychological symptoms, particularly anxiety, depression, and distress, were frequently identified as central and stably interconnected. Fatigue consistently emerged as a core symptom, closely linked to sleep disturbances, cognitive impairment, and emotional distress. Associations between symptoms and inflammatory biomarkers (eg, interleukin-6, C-reactive protein, and tumor necrosis factor-α) suggest a biological basis for symptom interconnectivity. Conclusions: NA consistently identified core symptoms, particularly psychological symptoms and fatigue, and associations with inflammatory biomarkers. NA may deepen the understanding of symptom interconnectivity and guide more effective interventions. However, further longitudinal homogeneous studies using standardized methodologies are needed.

Objectives: Primary care treatable visits in the Emergency Department (ED) are part of the different factors leading to the overcrowding. Their triage and diversion to alternative care centers could potentially help manage the increasing E inflow provided the establishment of an advanced triage to ensure patients' safety. We aim to suggest a new triage tool, PERSEE, and prove its feasibility, safety and performance. Methods: All self-referrals presented to the ED were triaged with the PERSEE algorithm: first, patients were classified with a five-level ED acuity scale and then evaluated by algorithms to determine their appropriate category (ED or Primary Care). Patients were eligible for a redirection if they were triaged by the acuity scale as level 3 or lower, considered as ambulatory patients and finally categorized as primary care patients. We defined appropriate redirections as patients requiring less than three emergency resources, no emergency-specific treatment and no hospitalization. Results: During the study, 1999 patients were admitted to the ED. Among those, 1333 patients were self-referred (66.9%) of whom 1167 patients were triaged as level 3 or below (58.6%) and 775 patients triaged as ambulatory (39.0%). Among the 775 patients, 200 patients were categorized as primary care treatable (10.0%) and thereby, as potentially eligible for a redirection. We noticed an error rate of 7%, sensitivity of 24.06% and specificity of 97.6%. The redirection rate reached 15% of the self-referrals. Conclusion: These results indicate that PERSEE triage could lead to a safe redirection and could be an efficient tool to reduce ED crowding provided several adjustments.

Since the beginning of the novel coronavirus outbreak, different strategies have been explored to stem the spread of the disease and appropriately manage patient flow. Triage, an effective solution...