China's low fertility may not hinder future prosperity.

China's low fertility is often presented as a major factor which will hinder its prosperity in the medium to long term. This is based on the assumed negative consequences of an increasing old-age dependency ratio: a simplistic measure of relative changing age structures. Based on this view, policies to increase fertility are being proposed after decades of birth restriction policies. Here, we argue that a purely age structure-based reasoning which disregards labor force participation and education attainment may be highly misleading. While fertility has indeed fallen to low levels, human capital accumulation has been very strong-especially among younger cohorts. Factoring in the effects of labor force participation and educational attainment on productivity, a measure called "productivity-weighted labor force dependency ratio" can more accurately capture the economic implications of demographic change. When using this ratio, a much more optimistic picture of the economic (and social) future of China can be envisaged.

Skills-adjusted human capital shows rising global gap.

Human capital, broadly defined as the skills acquired through formal education, is acknowledged as one of the key drivers of economic growth and social development. However, its measurement for the working-age populations, on a global scale and over time, is still unsatisfactory. Most indicators either only consider the quantity dimension of education and disregard the actual skills or are demographically inconsistent by applying the skills of the young cohorts in school to represent the skills of the working-age population at the same time. In the case of rapidly expanding or changing school systems, this assumption is untenable. However, an increasing number of countries have started to assess the literacy skills of their adult populations by age and sex directly. Drawing on this literacy data, and by using demographic backprojection and statistical estimation techniques, we here present a demographically consistent indicator for adult literacy skills, the skills in literacy adjusted mean years of schooling (SLAMYS). The measure is given for the population aged 20 to 64 in 185 countries and for the period 1970 to 2015. Compared to the conventional mean years of schooling (MYS)-which has strongly increased for most countries over the past decades, and in particular among poor countries-the trends in SLAMYS exhibit a widening global skills gap between low- and high-performing countries.

Years of good life is a well-being indicator designed to serve research on sustainability.

Sustainable development (SD) as popularized by the Brundtland Commission and politically enshrined in the Sustainable Development Goals has been the explicit focus of sustainability science. While there is broad agreement that the trend of human well-being (W) over time should serve as a sustainability criterion, the literature so far has mostly addressed this in terms of its determinants rather than focusing on W itself. There is broad agreement that an indicator for W should have multiple constituents, clearly going beyond gross domestic product. Here, we propose a tailor-made indicator to serve precisely this purpose following a set of specified desiderata, including its applicability to flexibly defined subnational populations by gender, place of residence, ethnicity, and other relevant characteristics. The indicator, years of good life (YoGL), reflects the evident fact that in order to be able to enjoy any quality of life, one has to be alive and thus is primarily based on life expectancy. However, since mere survival is not considered good enough, life years are counted conditional on meeting minimum standards in two dimensions: the objective dimension of capable longevity (consisting of being out of absolute poverty and enjoying minimal levels of physical and cognitive health) and the subjective dimension of overall life satisfaction. We illustrate the calculation of this indicator for countries and subpopulations at different stages of development and with different degrees of data availability.

The long shadow of early-change patterns: a 3-year follow-up after the use of a web-based intervention for mild to moderate depressive symptoms.

Web-based interventions can be effective in treating depressive symptoms. Patients with risk not responding to treatment have been identified by early change patterns. This study aims to examine whether early changes are superior to baseline parameters in predicting long-term outcome. In a randomized clinical trial with 409 individuals experiencing mild to moderate depressive symptoms using the web-based intervention deprexis, three latent classes were identified (early response after registration, early response after screening and early deterioration) based on early change in the first four weeks of the intervention. Baseline variables and these classes were included in a Stepwise Cox Proportional Hazard Multiple Regression to identify predictors associated with the onset of remission over 36-months. Early change class was a significant predictor of remission over 36 months. Compared to early deterioration after screening, both early response after registration and after screening were associated with a higher likelihood of remission. In sensitivity and secondary analyses, only change class consistently emerged as a predictor of long-term outcome. Early improvement in depression symptoms predicted long-term outcome and those showing early improvement had a higher likelihood of long-term remission. These findings suggest that early changes might be a robust predictor for long-term outcome beyond baseline parameters.

An investigation of treatment return after psychological therapy for depression and anxiety.

BACKGROUND: Some patients return for further psychological treatment in routine services, although it is unclear how common this is, as scarce research is available on this topic. AIMS: To estimate the treatment return rate and describe the clinical characteristics of patients who return for anxiety and depression treatment. METHOD: A large dataset (N=21,029) of routinely collected clinical data (2010-2015) from an English psychological therapy service was analysed using descriptive statistics. RESULTS: The return rate for at least one additional treatment episode within 1-5 years was 13.7%. Furthermore, 14.5% of the total sessions provided by the service were delivered to treatment-returning patients. Of those who returned, 58.0% continued to show clinically significant depression and/or anxiety symptoms at the end of their first treatment, while 32.0% had experienced a demonstrable relapse before their second treatment. CONCLUSIONS: This study estimates that approximately one in seven patients return to the same service for additional psychological treatment within 1-5 years. Multiple factors may influence the need for additional treatment, and this may have a major impact on service activity. Future research needs to further explore and better determine the characteristics of treatment returners, prioritise enhancement of first treatment recovery, and evaluate relapse prevention interventions.

Individual treatment selection for patients with post-traumatic stress disorder: External validation of a personalised advantage index.

OBJECTIVE: To test the predictive accuracy and generalisability of a personalised advantage index (PAI) model designed to support treatment selection for Post-Traumatic Stress Disorder (PTSD). METHOD: A PAI model developed by Deisenhofer et al. (2018) was used to predict treatment outcomes in a statistically independent dataset including archival records for N = 152 patients with PSTD who accessed either trauma-focussed cognitive behavioural therapy or eye movement desensitisation and reprocessing in routine care. Outcomes were compared between patients who received their PAI-indicated optimal treatment versus those who received their suboptimal treatment. RESULTS: The model did not yield treatment specific predictions and patients who had received their PAI-indicated optimal treatment did not have better treatment outcomes in this external validation sample. CONCLUSION: This PAI model did not generalise to an external validation sample.

Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy.

OBJECTIVE: Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Processing (NLP) could augment the measurement of emotions. The study explores the validity of sentiment analysis in psychotherapy transcripts. METHOD: We used a transformer-based NLP algorithm to analyze sentiments in 85 transcripts from 35 patients. Construct and criterion validity were evaluated using self- and therapist reports and process and outcome measures via correlational, multitrait-multimethod, and multilevel analyses. RESULTS: The results provide indications in support of the sentiments' validity. For example, sentiments were significantly related to self- and therapist reports of emotions in the same session. Sentiments correlated significantly with in-session processes (e.g., coping experiences), and an increase in positive sentiments throughout therapy predicted better outcomes after treatment termination. DISCUSSION: Sentiment analysis could serve as a valid approach to assessing the emotional tone of psychotherapy sessions and may contribute to the multimodal measurement of emotions. Future research could combine sentiment analysis with automatic emotion recognition in facial expressions and vocal cues via the Nonverbal Behavior Analyzer (NOVA). Limitations (e.g., exploratory study with numerous tests) and opportunities are discussed.

Love yourself as a therapist, doubt yourself as an institution? Therapist and institution effects on outcome, treatment length, and dropout.

OBJECTIVE: Research suggests that some therapists achieve better outcomes than others. However, an overlooked area of study is how institution differences impact patient outcomes independent of therapist variance. This study aimed to examine the role of institution and therapist differences in adult outpatient psychotherapy. METHOD: The study included 1428 patients who were treated by 196 therapists at 10 clinics. Two- and three-level hierarchical linear regression models were employed to investigate the effects of therapists and institutions on three dependent patient variables: (1) symptom change, (2) treatment duration, and (3) dropout. Level three explanatory variables were tested. RESULTS: The results showed that therapist effects (TE) were significant for all three types of treatment outcome (7.8%-18.2%). When a third level (institution) was added to the model, the differences between therapists decreased, and significant institution effects (IE) were found: 6.3% for symptom change, 10.6% for treatment duration, and 6.5% for dropout. The exploratory analyses found no predictors able to explain the systematic variation at the institution level. DISCUSSION: TE on psychotherapy outcomes remain a relevant factor but may have been overestimated in previous studies due to not properly distinguishing them from differences at the institution level.

The Effects of Switching to Video Therapy on In-Session Processes in Psychotherapy During the COVID-19 Pandemic.

OBJECTIVE AND AIM: This study aimed to assess the impact of switching from face-to-face (f2f) psychotherapy to video therapy (VT) due to the COVID-19 pandemic on in-session processes, i.e., the therapeutic alliance, coping skills, and emotional involvement, as rated by both patients and therapists. METHODS: A total of N = 454 patients with mood or anxiety disorders were examined. The intervention group (IG) consisted of n = 227 patient-therapist dyads, who switched from f2f to VT, while the control group (CG) consisted of n = 227 patient-therapist dyads, who were treated f2f before the pandemic. To evaluate the effects of switching to VT on in-session processes, three longitudinal piecewise multilevel models, one per process variable, were fitted. Each process variable was regressed on the session number with a slope for the three sessions before switching to VT and a second slope for up to six VT sessions afterwards. RESULTS: The therapeutic alliance significantly increased after switching from f2f to VT across the two groups (IG and CG) and raters (patients and therapists) with no differences between IG and CG. On average, patients rated the therapeutic alliance better than therapists. Coping skills significantly increased after switching from f2f to VT across the two groups and raters, but the CG rated coping skills higher than the IG after the switch. Overall, therapists rated coping skills higher than patients. Emotional involvement did not significantly increase after switching to VT across the two groups and raters and there was no significant difference between patient and therapist ratings. DISCUSSION: In conclusion, the switch to VT had no negative impact on the therapeutic alliance and emotional involvement. However, more coping skills were reported in the CG than in the IG after the switch to VT, which was mainly due to a stagnation in patient-rated coping skills in the IG.

Routine Outcome Monitoring and Clinical Feedback in Psychotherapy: Recent Advances and Future Directions.

In the past decade, there has been an increase in research related to the routine collection and active use of standardized patient data in psychotherapy. Research has increasingly focused on personalization of care to patients, clinical skills and interventions that modulate treatment outcomes, and implementation strategies, all of which appear to enhance the beneficial effects of ROM and feedback. In this article, we summarize trends and recent advances in the research on this topic and identify several essential directions for the field in the short to medium term. We anticipate a broadening of research from the focus on average effects to greater specificity around what kinds of feedback, provided at what time, to which individuals, in what settings, are most beneficial. We also propose that the field needs to focus on issues of health equity, ensuring that ROM can be a vehicle for increased wellbeing for those who need it most. The complexity of mental healthcare systems means that there may be multiple viable measurement solutions with varying costs and benefits to diverse stakeholders in different treatment contexts, and research is needed to identify the most influential components in each of these contexts.

Measuring Alliance and Symptom Severity in Psychotherapy Transcripts Using Bert Topic Modeling.

We aim to use topic modeling, an approach for discovering clusters of related words ("topics"), to predict symptom severity and therapeutic alliance in psychotherapy transcripts, while also identifying the most important topics and overarching themes for prediction. We analyzed 552 psychotherapy transcripts from 124 patients. Using BERTopic (Grootendorst, 2022), we extracted 250 topics each for patient and therapist speech. These topics were used to predict symptom severity and alliance with various competing machine-learning methods. Sensitivity analyses were calculated for a model based on 50 topics, LDA-based topic modeling, and a bigram model. Additionally, we grouped topics into themes using qualitative analysis and identified key topics and themes with eXplainable Artificial Intelligence (XAI). Symptom severity could be predicted with highest accuracy by patient topics ( r =0.45, 95%-CI 0.40, 0.51), whereas alliance was better predicted by therapist topics ( r =0.20, 95%-CI 0.16, 0.24). Drivers for symptom severity were themes related to health and negative experiences. Lower alliance was correlated with various themes, especially psychotherapy framework, income, and everyday life. This analysis shows the potential of using topic modeling in psychotherapy research allowing to predict several treatment-relevant metrics with reasonable accuracy. Further, the use of XAI allows for an analysis of the individual predictive value of topics and themes. Limitations entail heterogeneity across different topic modeling hyperparameters and a relatively small sample size.

Using Progress Feedback to Enhance Treatment Outcomes: A Narrative Review.

We face increasing demand for greater access to effective routine mental health services, including telehealth. However, treatment outcomes in routine clinical practice are only about half the size of those reported in controlled trials. Progress feedback, defined as the ongoing monitoring of patients' treatment response with standardized measures, is an evidence-based practice that continues to be under-utilized in routine care. The aim of the current review is to provide a summary of the current evidence base for the use of progress feedback, its mechanisms of action and considerations for successful implementation. We reviewed ten available meta-analyses, which report small to medium overall effect sizes. The results suggest that adding feedback to a wide range of psychological and psychiatric interventions (ranging from primary care to hospitalization and crisis care) tends to enhance the effectiveness of these interventions. The strongest evidence is for patients with common mental health problems compared to those with very severe disorders. Effect sizes for not-on-track cases, a subgroup of cases that are not progressing well, are found to be somewhat stronger, especially when clinical support tools are added to the feedback. Systematic reviews and recent studies suggest potential mechanisms of action for progress feedback include focusing the clinician's attention, altering clinician expectations, providing new information, and enhancing patient-centered communication. Promising approaches to strengthen progress feedback interventions include advanced systems with signaling technology, clinical problem-solving tools, and a broader spectrum of outcome and progress measures. An overview of methodological and implementation challenges is provided, as well as suggestions for addressing these issues in future studies. We conclude that while feedback has modest effects, it is a small and affordable intervention that can potentially improve outcomes in psychological interventions. Further research into mechanisms of action and effective implementation strategies is needed.

Population aging, migration, and productivity in Europe.

This paper provides a systematic, multidimensional demographic analysis of the degree to which negative economic consequences of population aging can be mitigated by changes in migration and labor-force participation. Using a microsimulation population projection model accounting for 13 individual characteristics including education and immigration-related variables, we built scenarios of future changes in labor-force participation, migration volumes, and their educational composition and speed of integration for the 28 European Union (EU) member states. We study the consequences in terms of the conventional age-dependency ratio, the labor-force dependency ratio, and the productivity-weighted labor-force dependency ratio using education as a proxy of productivity, which accounts for the fact that not all individuals are equality productive in society. The results show that in terms of the more sophisticated ratios, population aging looks less daunting than when only considering age structure. In terms of policy options, lifting labor-force participation among the general population as in Sweden, and education-selective migration if accompanied by high integration, could even improve economic dependency. On the other hand, high immigration volumes combined with both low education and integration leads to increasing economic dependency. This shows the high stakes involved with integration outcomes under high migration volumes.

[Measurement Invariance and Validity of the Short Form of the Bielefeld Partnership Expectations Questionnaire (BPEQ12)]. / Messinvarianz und Validität der Kurzform des Bielefelder Fragebogens zu Partnerschaftserwartungen (BFPE12).

The short form of the Bielefeld Partnership Expectations Questionnaire (BPEQ12) measures three partner-related attachment scales: fear of rejection, readiness for self-disclosure and conscious need for care. In addition to factor structure and reliability, the present study examined measurement invariance and validity using a non-clinical and a clinical sample of college students (N=208). Besides the BFPE12, the following Questionnaires were assessed: Short Form of Experiences in Close Relationships - Revised (ECR-RD8), Outcome Questionnaire (OQ-30), revised Beck Depression Inventory (BDI-II), Social Phobia Inventory (SPIN), and Test Anxiety Inventory (TAI-G). The factor structure is tested using confirmatory factor analysis (CFA), the internal consistency of the scales is quantified using McDonald's ω, the measurement invariance is investigated with two-group structural equation models, and the validity is examined using correlation and regression analyses. In both samples, the factor structure was confirmed (CFI>0.93; TLI>0.93; RMSEA<0.08; SRMR<0.08) and the reliability of all three scales was acceptable (ω>.7) - with the exception of need for care in the non-clinical group. We found configurational, metric and scalar measurement invariance regarding to the assignment in the clinical and non-clinical sample. In terms of convergent validity, fear of rejection and conscious need for care were associated with attachment-related anxiety (r=0.771 and r=0.539, p<0.001) and low readiness for self-disclosure was correlated with attachment-related avoidance (measured with ECR-RD8, r=- 0.704, p<0.001). Overall, the present study supports the factor structure, measurement invariance, reliability, and validity of the BPEQ12 in clinical and non-clinical samples.

Dynamic and static predictive modelling of psychotherapy outcome-Comparison of two statistical approaches.

OBJECTIVE: Improving prediction abilities in the therapy process can increase therapeutic success for a variety of reasons, such as more personalised treatment or resource optimisation. The increasingly applied methods of dynamic prediction seem to be very promising for this purpose. Prediction models are usually based on static approaches of frequentist statistics. However, the application of this statistical approach has been widely criticised in this research area. Bayesian statistics has been proposed in the literature as an alternative, especially for the task of dynamic modelling. In this study, we compare the performance of predicting therapy outcome over the course of therapy between both statistical approaches. METHOD: Based on a sample of 341 patients, a logistic regression analysis was performed using both statistical approaches. Therapy success was conceptualised as reliable pre-post improvement in brief symptom inventory (BSI) scores. As predictors, we used the subscales of the Outcome Questionnaire (OQ-30) and the Helping Alliance Questionnaire (HAQ) measured every fifth session, as well as baseline BSI scores. RESULTS: The influence of the predictors during therapy differs between the frequentist and the Bayesian approach. In contrast, predictive validity is comparable with a mean area under the curve (AUC) of 0.76 in both model types. CONCLUSION: Bayesian statistic provides an innovative and useful alternative to the frequentist approach in predicting therapy outcome. The theoretical foundation is particularly well suited for dynamic prediction. Nevertheless, no differences in predictive validity were found in this study. More complex methodology as well as further research seems necessary to exploit the potential of Bayesian statistics in this area.

Is it me, is it you or is it both of US? Applying the social relations model to disentangle the components of the therapeutic bond.

OBJECTIVE: The study investigated the contribution of therapists and patients to the therapeutic bond and their associations (at the within and between levels) to treatment outcome. On this aim, the social relations model (SRM, aimed to analyze dyadic interpersonal data) was implemented. METHOD: A novel design for individual psychotherapy studies was adopted, a many-with-many asymmetrical block dyadic design, in which several patients interact with several therapists. Hierarchical linear models were computed to study through variance partitioning the different components of the SRM and their association to treatment outcome. RESULTS: All SRM components (with significant effects at therapist- and patient- within and between levels) resulted in significant contributions to the bond. However, only components at the within- and between-therapist, and within-patient levels resulted in significant associations with outcome. CONCLUSION: Given the dyadic nature of the bond, our results support not only studying and offering clinical training on interpersonal therapeutic skills but also on constant monitoring and feedback of the relationship at the more idiosyncratic level.

Routine Outcome Monitoring (ROM) and Feedback: Research Review and Recommendations.

OBJECTIVE: To provide a research review of the components and outcomes of routine outcome monitoring (ROM) and recommendations for research and therapeutic practice. METHOD: A narrative review of the three phases of ROM - data collection, feeding back data, and adapting therapy - and an overview of patient outcomes from 11 meta-analytic studies. RESULTS: Patients support ROM when its purpose is clear and integrated within therapy. Greater frequency of data collection is more important for shorter-term therapies, and use of graphs, greater specificity of feedback, and alerts are helpful. Overall effects on patient outcomes are statistically significant (g ≈ 0.15) and increase when clinical support tools (CSTs) are used for not-on-track cases (g ≈ 0.36-0.53). Effects are additive to standard effects of psychological therapies. Organizational, personnel, and resource issues remain the greatest obstacles to the successful adoption of ROM. CONCLUSION: ROM offers a low-cost method for enhancing patient outcomes, on average resulting in an ≈ 8% advantage (success rate difference; SRD) over standard care. CSTs are particularly effective for not-on-track patients (SRD between ≈ 20% and 29%), but ROM does not work for all patients and successful implementation is a major challenge, along with securing appropriate cultural adaptations.

Predicting dropout from psychological treatment using different machine learning algorithms, resampling methods, and sample sizes.

Objective: The occurrence of dropout from psychological interventions is associated with poor treatment outcome and high health, societal and economic costs. Recently, machine learning (ML) algorithms have been tested in psychotherapy outcome research. Dropout predictions are usually limited by imbalanced datasets and the size of the sample. This paper aims to improve dropout prediction by comparing ML algorithms, sample sizes and resampling methods. Method: Twenty ML algorithms were examined in twelve subsamples (drawn from a sample of N = 49,602) using four resampling methods in comparison to the absence of resampling and to each other. Prediction accuracy was evaluated in an independent holdout dataset using the F1-Measure. Results: Resampling methods improved the performance of ML algorithms and down-sampling can be recommended, as it was the fastest method and as accurate as the other methods. For the highest mean F1-Score of .51 a minimum sample size of N = 300 was necessary. No specific algorithm or algorithm group can be recommended. Conclusion: Resampling methods could improve the accuracy of predicting dropout in psychological interventions. Down-sampling is recommended as it is the least computationally taxing method. The training sample should contain at least 300 cases.

Applying precision methods to treatment selection for moderate/severe depression in person-centered experiential therapy or cognitive behavioral therapy.

OBJECTIVE: To develop two prediction algorithms recommending person-centered experiential therapy (PCET) or cognitive-behavioral therapy (CBT) for patients with depression: (1) a full data model using multiple trial-based and routine variables, and (2) a routine data model using only variables available in the English NHS Talking Therapies program. METHOD: Data was used from the PRaCTICED trial comparing PCET vs. CBT for 255 patients meeting a diagnosis of moderate or severe depression. Separate full and routine data models were derived and the latter tested in an external data sample. RESULTS: The full data model provided the better prediction, yielding a significant difference in outcome between patients receiving their optimal vs. non-optimal treatment at 6- (Cohen's d = .65 [.40, .91]) and 12 months (d = .85 [.59, 1.10]) post-randomization. The routine data model performed similarly in the training and test samples with non-significant effect sizes, d = .19 [-.05, .44] and d = .21 [-.00, .43], respectively. For patients with the strongest treatment matching (d ≥ 0.3), the resulting effect size was significant, d = .38 [.11, 64]. CONCLUSION: A treatment selection algorithm might be used to recommend PCET or CBT. Although the overall effects were small, targeted matching yielded somewhat larger effects.

Process-Based psychotherapy personalization: considering causality with continuous-time dynamic modeling.

Psychotherapy can be improved by integrating the study of mediators (how it works) and moderators (for whom it works). To demonstrate this integration, we studied the relationship between resource activation, problem-coping experiences and symptoms in cognitive-behavior therapy (CBT) for depression, to obtain preliminary insights on causal inference (which process leads to symptom improvement?) and prediction (which one for whom?).A sample of 715 patients with depression who received CBT was analyzed. Hierarchical Bayesian continuous time dynamic modeling was used to study the temporal dynamics between the variables analyzed within the first ten sessions. Depression and self-efficacy at baseline were examined as predictors of these dynamics.There were significant cross-effects between the processes studied. Under typical assumptions, resource activation had a significant effect on symptom improvement. Problem-coping experience had a significant effect on resource activation. Depression and self-efficacy moderated these effects. However, when system noise was considered, these effects may be affected by other processes.Resource activation was strongly associated with symptom improvement. To the extent of inferring causality, for patients with mild-moderate depression and high self-efficacy, promoting resource activation can be recommended. For patients with severe depression and low self-efficacy, promoting problem-coping experiences can be recommended.