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Objective:
Therapist competence is considered essential for the success of psychotherapy. Feedback is an intervention which has the potential to improve therapist competence. The present study investigated whether competence feedback leads to an improvement of therapist competence and patient outcome.
Method:
Sixty-seven master-level clinical trainees were randomly assigned to either a competence feedback group (CFG) or a control group (CG). Patients with a diagnosis of major depression (N = 114) were randomly assigned to CFG or CG. Treatment included 20 individual sessions of cognitive behavioral therapy (CBT). In CFG, therapists received, parallel to the treatment, five competence feedbacks, based on videotaped therapy sessions. Independent raters assessed therapist competence with the Cognitive Therapy Scale (CTS) and provided the competence feedback. Patient outcome was evaluated with the Beck Depression Inventory-II (BDI-II) and therapeutic alliance (Helping Alliance Questionnaire [HAQ]) from both therapist's (HAQ-T) and patient's (HAQ-P) perspective were evaluated after each of the 20 sessions.
Results:
(a) Therapist competence (CTS) increased significantly more for CFG than CG. (b) Depression (BDI-II) decreased significantly across sessions for both groups, but without evidence for a group-differential benefit for the CFG. (c) Therapeutic alliance (HAQ-T/P) increased significantly across sessions for both groups from both perspectives, but without group differences. (d) There is a positive effect of BDI-II on CTS at the beginning and a negative effect of CTS on BDI-II at the end of therapy.
Conclusion:
Competence feedback improves therapists' independently rated competence, but there is no evidence that competence feedback in CBT leads to better outcome.
What is the public health significance of this article? This study suggests the substantial value of systematic competence feedback for improving therapist competence in the psychotherapy of depression. No significant effect of competence feedback on the reduction of reported depressive symptoms was found.
Previous studies (Hyona, Yan, & Vainio, 2018; Yan et al., 2014) have demonstrated that in morphologically rich languages a word's morphological status is processed parafoveally to be used in modulating saccadic programming in reading. In the present parafoveal preview study conducted in Finnish, we examined the exact nature of this effect by comparing reading of morphologically complex words (a stem + two suffixes) to that of monomorphemic words. In the preview-change condition, the final 3-4 letters were replaced with other letters making the target word a pseudoword; for suffixed words, the word stem remained intact but the suffix information was unavailable; for monomorphemic words, only part of the stem was parafoveally available. Three alternative predictions were put forth. According to the first alternative, the morphological effect in initial fixation location is due to parafoveally perceiving the suffix as a highly frequent letter cluster and then adjusting the saccade program to land closer to the word beginning for suffixed than monomorphemic words. The second alternative, the processing difficulty hypothesis, assumes a morphological complexity effect: suffixed words are more complex than monomorphemic words. Therefore, the attentional window is narrower and the saccade is shorter. The third alternative posits that the effect reflects parafoveal access to the word's stem. The results for the initial fixation location and fixation durations were consistent with the parafoveal stem-access view.
Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics
(2021)
Newly emerging pandemics like COVID-19 call for predictive models to implement precisely tuned responses to limit their deep impact on society. Standard epidemic models provide a theoretically well-founded dynamical description of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which creates challenges for containment strategies. However, spatial heterogeneity raises questions about the adequacy of modeling epidemic outbreaks on the level of a whole country. Here, we show that by applying sequential data assimilation to the stochastic SEIR epidemic model, we can capture the dynamic behavior of outbreaks on a regional level. Regional modeling, with relatively low numbers of infected and demographic noise, accounts for both spatial heterogeneity and stochasticity. Based on adapted models, short-term predictions can be achieved. Thus, with the help of these sequential data assimilation methods, more realistic epidemic models are within reach.