درخت حوزه‌های تخصصی

Objective: A great number of patients leave psychotherapy prior to the end of treatment. The present study predicts the premature termination of individual psychotherapy based on patients’ expectations and tolerance and therapists’ competencies with the mediating role of therapeutic alliance. Method: In this descriptive cross-sectional study, 317 patients referred to Mental Health Clinics, Pain Clinics and Substance Use Treatment Centers in Mashhad, Iran were selected by random sampling. The data were collected by Distress/Endorsement Validation Scale, Working Alliance Inventory-Short Revised, the Milwaukee Psychotherapy Expectations Questionnaire, and Outcome Questionnaire. The data were analyzed by SPSS-19 and structural equation modeling with LISREL-8.80 software. Results: The examined model indicated good fitness for the data observed (RMSEA=0.02, GFI=0.99, AGFI=0.99, NFI=1, CFI=1, IFI=1, RMR=0.00253, P value=0.90). Psychotherapy expectations (γ=-0.40) and therapists’ competencies (γ=-0.29) directly influenced the premature termination of individual psychotherapy. Treatment tolerance (γ=-0.17) directly affected the premature termination of individual psychotherapy. Therapeutic alliance (β=-0.13) had an impact on the premature termination of individual psychotherapy. Psychotherapy expectations (γ=0.42) and therapists’ competencies (γ=0.54) significantly influenced the therapeutic alliance, thereby affecting the premature termination of individual psychotherapy. Conclusions: Psychotherapy expectations, treatment tolerance, therapists’ competencies and therapeutic alliance interactively effect in the premature termination of psychotherapy and therefore on the health outcomes of patients. This finding did develop insights into designing the tailored interventions to resolve premature termination and improve the outcomes of psychotherapeutic interventions. Furthermore, clinicians must be concerned with these factors in the clinics and healthcare centers to enhance the successful termination of psychotherapy.

We used the affective profiles model to investigate individual differences in motivation, stress and energy. The aim was to replicate past findings, but we also focused on matched comparisons within individuals with affective profiles that are similar in one affective dimension and differ in the other in order to predict changes when individuals increase/decrease their experience of positive or negative affect. Methods: <span class="fontstyle2">A total of 567 participants answered the Positive Affect and Negative Affect Schedule, which was used for affective profiling; the Situational Motivation Scale, which measures intrinsic motivation, identified regulation, external regulation, and amotivation; and the Stress-Energy questionnaire. Results: <span class="fontstyle2">Comparisons between the four different profiles, replicating the past findings, showed that individuals with high affective and self-fulfilling profile scored highest in intrinsic motivation, identified regulation, and energy, while they scored lowest in external motivation, amotivation, and the self-fulfilling profile, also lowest in stress. Additionally, the matched comparisons showed, for example, that levels of intrinsic motivation increase when negative affect levels decrease, and positive affect is kept high when positive affect decreases and negative affect is kept low. Conclusions: One important feature of the affective profiles model is the possibility to compare individuals that are similar in one affect dimension but differ in the other (Garcia, 2011, 2017). This way of discussing individual differences helps to predict what changes could be expected when individuals increase or decrease their experience of positive or negative affect. Importantly, the direction of these changes cannot be addressed from cross-sectional data

Objective: The present study aimed to predict corona disease anxiety among medical staff in Tehran based on five-factor theory of personality. Method: In the present descriptive and correlational study, the statistical population included all medical staff in Tehran in 2020, among which 210 physicians, nurse, and other medical staffs were selected based on snowball sampling method. The instruments included Neo-Five Factor Inventory (NEO-FFI), Corona Disease Anxiety Scale (CDAS). Data were analyzed using Pearson’s correlation and Multiple Regression Analysis. Findings: Regression analysis indicated significantly Beta coefficients for openness personality traits (-0.238) and conscientiousness personality traits (-0.249). a statistically significant correlation is observed between openness (39%), neuroticism (-39%), extraversion (37%), conscientiousness (34%), and agreeableness (25%) with coronavirus anxiety at the 99% confidence level. Further, the correlation between each of the dimensions and the mental component of coronavirus anxiety is more compared to that of physical one. Conclusion: Based on the results of multiple regression analysis, the micro-components of openness and conscientiousness could predict the level of total coronavirus anxiety. Considering standard coefficients, conscientiousness plays a more important role in predicting the level of total coronavirus anxiety in medical staffs, which decreases by increasing openness and conscientiousness. Medical staff who are higher in openness and conscientiousness personality traits are lower in corona disease anxiety.