Browsing by Author "Delgadillo, Jaime"

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    The Importance of Conducting Practice-oriented Research with Underserved Populations
    (2024) Fernandez-Alvarez, Javier; Molinari, Guadalupe; Kilcullen, Ryan; Delgadillo, Jaime; Drill, Rebecca; Errazuriz, Paula; Falkenstrom, Fredrik; Firth, Nick; O'Shea, Amber; Paz, Clara; Youn, Soo Jeong; Castonguay, Louis G.
    There has been a growing emphasis on dissemination of empirically supported treatments. Dissemination, however, should not be restricted to treatment. It can and, in the spirit of the scientific-practitioner model, should also involve research. Because it focuses on the investigation of clinical routine as it takes place in local settings and because it can involve the collaboration of several stakeholders, practice-oriented research (POR) can be viewed as an optimal research method to be disseminated. POR has the potential of addressing particularly relevant gaps of knowledge and action when implemented in regions of the world that have limited resources for or experiences with empirical research, and/or in clinical settings that are serving clinical populations who are not typically receiving optimal mental care services - specifically, individuals in rural and inner cities that have limited economic and social resources. The establishment and maintenance of POR in such regions and/or settings, however, come with specific obstacles and challenges. Integrating the experiences acquired from research conducted in various continents (Africa, Europe, Latin America, and North America), the goal of this paper is to describe some of these challenges, strategies that have been implemented to address them, as well as new possible directions to facilitate the creation and growth of POR. It also describes how these challenges and ways to deal with them can provide helpful lessons for already existing POR infrastructures.
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    Understanding Symptom Profiles of Depression with the PHQ-9 in a Community Sample Using Network Analysis
    (2024) Núñez Barraza, Catalina Andrea; Delgadillo, Jaime; Barkham, Michael; Behn Berliner, Alex Joseph
    Background: Depression is one of the most prevalent mental health conditions in the world. However, the heterogeneity of depression has presented obstacles for research concerning disease mechanisms, treatment indication, and personalization. So far, depression heterogeneity research has mainly used latent variable modeling, assuming a latent cause, that overlooks the possibility that symptoms might interact and reinforce each other. The current study used network analysis to analyze and compare profiles of depressive symptoms present in community samples, considering the relationship between symptoms. Methods: Cross-sectional measures of depression using the Patient Health Questionnaire-9 (PHQ-9) were collected from community samples using data from participants scoring above a clinical threshold of ≥10 points (N=2,023; 73.9% female; mean age 49.87, SD= 17.40). Data analysis followed three steps. First, a profiling algorithm was implemented to identify all possible symptom profiles by dichotomizing each PHQ-9 item. Second, the most prevalent symptom profiles were identified in the sample. Third, network analysis for the most prevalent symptom profiles was carried out to identify the centrality and covariance of symptoms. Results: Of 382 theoretically possible depression profiles, only 167 were present in the sample. Furthermore, 55.6% of the symptom profiles present in the sample were represented by only eight profiles. Network analysis showed that the network and symptoms relationship varied across the profiles. Conclusions: Findings indicate that the vast number of theoretical possible ways to meet the criteria for major depressive disorder is significantly reduced in empirical samples, and that the most common profiles of symptoms have different networks and connectivity patterns. Scientific and clinical consequences of these findings are discussed in the context of the limitations of this study.