Effectiveness of the application of the Echomantra in an adolescent girl with anorexia nervosa and her caregiver: A case study. The aim of the ECHOMANTRA program is to facilitate the transition from hospital back into the community. ECHOMANTRA is based on interventions for carers (Experienced Carers Helping Others, ECHO; Treasure et al. 2016) and patients (Maudsley Model of Anorexia Nervosa Treatment for Adults, MANTRA; Schmidt, et al., 2014). The aim of this study was to evaluate the effectiveness of the ECHOMANTRA program, applied together with the usual treatment, in a 15-yearold adolescent girl with anorexia nervosa (AN) and her mother. A single case design and pre-post measures were used, as well as follow-up at 3 and 6 months. The patient was assessed for: eating pathology (EDE-Q), emotional state (DASS-21), psychosocial adjustment (EQ-5D-5L and CIA 3.0) and motivation to change. In the mother: expressed emotion (FQ), symptom impact (EDSIS), accommodation to illness (EAISA), emotional state (DASS-21) and her caregiver skills (CSS) were assessed. Both programs consisted of 8 on-line sessions, which were conducted individually and on a weekly basis. The results showed a reduction in AN symptomatology, increased BMI, improved emotional state, motivation to change and psychosocial adjustment; and in the mother, improved emotional state and caregiving skills, and reduced accommodation to illness, expressed emotion and the impact of symptoms. These changes were maintained at follow-up. Both patient and family valued the program as satisfactory. Both the acceptability and efficiency of treatment for AN may be improved by using ECHOMANATRA to prepare for transition from inpatient care, by giving support to both patients and their carers.

Given the socioeconomic and environmental differences between Mexico’s geographical regions, having a multi-campus system is common for private and public universities. Hence, students may choose to migrate from one campus to another. Although such a phenomenon is not properly students’ desertion, students’ migration impacts campus’ main indicators: enrollment growth goals, terminal efficiency, accreditation programs, and revenue. Thus, the campus of origin internalizes migration as students’ desertion. By considering a campus from a private multi-campus university in Baja California, Mexico, this study characterizes and predicts students’ migration and predicts by determining the socioeconomic and academic variables that impact the probability of moving to a different campus. Our database comprises quantitative and qualitative information of 356 dropout students from 2008 to 2018. Hence, we apply the logistic regression technique to build a predictive model; we found that the most significant predictive variables are the GPA results, age, financial support, and academic development. So, our main results characterize migrant students as having top grades, coming from the high school campus, and attending engineering programs. Surprisingly, economic variables are not significant in choosing to migrate from one campus to another.

Given the socioeconomic and environmental differences between Mexico’s geographical regions, having a multi-campus system is common for private and public universities. Hence, students may choose to migrate from one campus to another. Although such a phenomenon is not properly students’ desertion, students’ migration impacts campus’ main indicators: enrollment growth goals, terminal efficiency, accreditation programs, and revenue. Thus, the campus of origin internalizes migration as students’ desertion. By considering a campus from a private multi-campus university in Baja California, Mexico, this study characterizes and predicts students’ migration and predicts by determining the socioeconomic and academic variables that impact the probability of moving to a different campus. Our database comprises quantitative and qualitative information of 356 dropout students from 2008 to 2018. Hence, we apply the logistic regression technique to build a predictive model; we found that the most significant predictive variables are the GPA results, age, financial support, and academic development. So, our main results characterize migrant students as having top grades, coming from the high school campus, and attending engineering programs. Surprisingly, economic variables are not significant in choosing to migrate from one campus to another.