Cristian Cáceres

Post-disaster humanitarian logistics faces major challenges related to uncertainty, volatile aid flows, and material convergence. Although donation prediction has advanced at aggregate and organizational levels, individual-level behavioral and psychosocial determinants remain insufficiently integrated into predictive frameworks. This study proposes a predictive extension of a previously validated socio-technical framework, integrating outcome-free SEM-derived latent constructs, donor profiles, and questionnaire variables into a machine learning layer to anticipate donation frequency and resource type.The empirical analysis uses 362 valid observations from adults in rural municipalities of the Maule Region, Chile. Several predictor configurations and model families were evaluated, including multinomial logistic regression, ensemble models, and benchmark classifiers, complemented by SHAP-based interpretability analysis. Results show that psychosocial variables contain meaningful predictive signal. For donation frequency, the best-performing models achieve Quadratic Weighted Kappa (QWK) values close to 0.30. For donation type, Random Forest models using classifier chains obtain the strongest performance, with a macro-averaged F1 score close to 0.59. SHAP results indicate that frequency is mainly discriminated by granular resilience and motivation items, whereas perceived constraints and donation-type dependencies help predict resource composition.These findings provide behavioral inputs for anticipating donation intensity, resource composition, and aid-flow congestion in post-disaster contexts.

Background: Mental toughness (MT) is a key psychological resource for performing under pressure in youth and university sport. Contemporary theory conceptualizes MT as multidimensional, rather than a single global trait. This study aimed to provide a Spanish adaptation of the Mental Toughness Scale (MTS) and to examine its multidimensional structure, reliability, and decision utility for screening and follow-up in university sport. Methods: Chilean university athletes completed a Spanish back-translated MTS and a general self-efficacy (GSE) measure. Competing structures were tested using Exploratory Structural Equation Modeling (ESEM; WLSMV, GEOMIN). A bifactor model was additionally estimated to evaluate total-score interpretability (ECV, ωH). Internal consistency (McDonald’s ω) and concurrent validity (MTS–GSE correlations) were estimated. Receiver Operating Characteristic (ROC) analyses using Youden’s J identified a pragmatic, specificity-oriented threshold to support exploratory screening, profiling, and follow-up decisions in university sport. Results: A two-factor, 10-item solution showed excellent fit (RMSEA = 0.04; CFI = 0.99; TLI = 0.98) and outperformed unidimensional alternatives, supporting the multidimensionality of MT in line with established conceptual models. Internal consistency was excellent (ω = 0.90). MT correlated moderately with GSE ( r = .57, p < .001). ROC analysis yielded AUC = 0.80 (95% CI 0.75–0.85). A cut-off of ≥ 49.5 optimized specificity (~ 95%) with acceptable sensitivity (~ 55%), supporting an exploratory, conservative approach to screening and follow-up in applied settings. The bifactor model also showed excellent fit (RMSEA = 0.044; CFI = 0.994; TLI = 0.990), with the general factor accounting for most common variance (ECV = 0.73) and high general-factor reliability (ωH = 0.90), suggesting that the total score largely reflects a general mental toughness construct while the specific factors capture meaningful components. Conclusions: The Spanish 10-item, two-factor MTS provides a brief, reliable, and theoretically coherent assessment of MT, capturing its multidimensional nature in university sport. Beyond psychometric adequacy, a specificity-oriented cut-off offers actionable guidance for screening, referral, and monitoring. The translation and analytic protocol is readily replicable and can scaffold multi-site benchmarking and future measurement invariance across Spanish-speaking contexts.

Since its first definition, the number of dimensions of the posttraumatic stress disorder (PTSD) has increased (e.g., 1 to 7). This could be caused by 2 methodological reasons. First, the studies do not select representative samples of the population and do not consider the magnitude of exposure to the same event. Second, they use nonrobust data analysis, which increases the number of dimensions artificially. The aim is to determine how many dimensions PTSD has, when both sources of error are controlled. From 25,400 representative participants of the same population exposed to an earthquake and evaluated with the Davidson Trauma Scale, 127 smaller samples of 200 participants were obtained and classified according to the Mercalli magnitude. Parallel factor analyses (PFA), exploratory structural equations models (ESEM), and classic exploratory factor analysis (EFA) were applied to each sample. There were significant differences in fit and number of dimensions between the high and low Mercalli magnitude. In high exposure conditions: There were 1 dimension according to PFA and 3 according to EFA; ESEM and CFA indicated good fit. The main finding was that PTSD has a unique dimension that includes all of its symptoms. The need to use robust methods of analysis to evaluate data from samples homogeneously exposed to the same potentially traumatic event is argued. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Solastalgia is a recent concept that refers to disruptive psychological responses in people exposed to environmental degradation. The aim of this study was to determine the number of dimensions solastalgia has using a sample of people exposed to the effects of climate change in the coastal dry land of Maule region, Chile. In order to achieve this, a Scale Of Solastalgia (SOS) was designed and then validated, by means of applying it to 223 inhabitants at the municipalities of Pencahue (n = 105) and Curepto (n = 118), who were also evaluated by the Short Post-traumatic Stress Disorder Rating Interview (SPRINT-E). Using robust validation methods (Parallel factor analysis and Omega), two dimensions were obtained for solastalgia: solace and algia. Both correlate with the SPRINT-E scale (r = 0.150, p < 0.01 and r = 0.359, p < 0.01, respectively) and have 58% sensitivity and 67% specificity to detect cases of post-traumatic stress disorder (PTSD). Like PTSD, solastalgia is related to psychopathologies expected after disasters and also presents a spatial pattern where the concentration of positive cases occurs in places of greater exposure to environmental change or degradation.

Progressive changes in local environmental scenarios, accelerated by global climate change, can negatively affect the mental health of people who inhabit these areas. The magnitude of these effects may vary depending on the socioeconomic conditions of people and the characteristics of the environment, so certain territories can be more vulnerable than others. In this context, the present study aimed to geographically analyse the levels of psychosocial impact and the types of disruptive responses related to the new territorial scenarios caused by climate change in the coastal drylands of the Maule region, Chile. For this purpose, 223 people from two communes (Curepto and Pencahue) were psychosocially evaluated for post-traumatic stress disorder (PTSD) together with a survey of the prevailing sociodemographic and socioeconomic conditions in relation to the environmental variables of the territory. All information was georeferenced, stored within an ArcGIS Desktop geographic information system (GIS) and then investigated by application of contingency tables, ANOVA and local clustering analysis using SSP statistical software. The results indicated a high level of PTSD in the population, with significant differences related to age and education as well as employment conditions and income. The spatial results showed high PTSD values in the communal capital of Curepto in the central agricultural valley near the estuary of the local river, while the existence of coldspots was observed in the central valley of the Pencahue commune. It was concluded that proximity to population centres and surface water sources played the greatest role for the development of PTSD.

Objective. To establish differentiated depressive subtypes using a latent class analysis (LCA), including clinical and functional indicators in a sample of depressed patients consulted in Chilean Primary Health Care. Methods. A LCA was performed on a sample of 297 depressed patients consulted in Chilean PHC. The Mini International Neuropsychiatric Interview, the Hamilton Depression Rating Scale, the Outcome Questionnaire -social role, and interpersonal subscales were as instruments. A regression analysis of the different subtypes with sociodemographic and adverse life experiences was performed. Results. In a sample characterized by 87.5% of women, two, three, and four latent class models were obtained. The three-class model likely represents the best clinical implications. In this model, the classes were labeled: “complex depression” (CD) (58% of the sample), “recurrent depression” (RD) (34%), and “single depression episode” (SD) (8%). Members of CD showed a higher probability of history of suicide attempts, interpersonal, and social dysfunction. Psychiatric comorbidities differentiated the RD from SD. According to a multinomial regression model, childhood trauma experiences, recent stressful life experiences, and intimate partner violence events were associated with the CD class ( p < 0.01 ). Limitations. The vast majority of participants were females from Chile and the sample studied was not random. So, the results may not necessarily represent outpatient clinics. Conclusions. This study can provide additional evidence that depression, specifically in female gender, could be better understood as a complex heterogeneous disorder when clinical and functional indicators are studied. Furthermore, adverse life experiences starting in childhood could lead to a differentiated complex depressive subtype.

Los desastres se caracterizan por generar un impacto en las personas y comunidades expuestas, quienes responderán de manera disruptiva o saludable. El objetivo de esta investigación es definir ese impacto psicosocial mediante el diseño y validación de una escala: SPSI-D. Para eso: se revisó literatura centrada en el impacto de los desastres; se diseñaron ítems que fueron evaluados por jueces-expertos; y se validó la escala en una muestra de personas expuestas al terremoto y tsunami del 27 de Febrero de 2010 en Chile. Se utilizó el análisis factorial paralelo, confirmatorio y curvas ROC. Los resultados evidencian la validez, fiabilidad y precisión de la SPSI-D, la que quedó conformada por 51 ítems, agrupados en 6 dimensiones. El instrumento integra las ventajas de medir respuestas disruptivas y saludables después de un desastre. Se deja la SPSI-D a disposición de los profesionales que la requieran.