Transactional Analysis and Experiential Psychotherapy
Transactional analysis (TA) and experiential psychotherapy share many values and principles. This article explores some of these concepts and shares practical examples to explain their use in experiential psychotherapy. Topics covered include planning, diversity, and relationships. In particular, it explores the concept of dealing with ruptures in a relationship.
ML predicts alliance ratings from session content
ML can predict alliance ratings from session content and symptom levels. The present study evaluated the relationship between therapeutic alliance and symptom levels. The study also controlled for any change in the symptoms prior to the alliance assessment. The results indicate that alliance ratings can predict subsequent symptomatic change, but the findings are not specific. Future research should explore the characteristics of sessions where the alliance was unusually high or low.
The authors conducted an inductive analysis on the transcripts. They coded each transcript separately and together, and came up with consensual codes. They then compared their results to initial notes on the watching sessions. This ensures the credibility of the results. This method was tested on a dataset of more than a thousand sessions from a range of different industries.
The results showed a significant difference between sessions of high and low alliance. High-alliance sessions were marked by explicit focus while low-alliance sessions were marked by a lack of explicit focus. The study also looked for deviant sessions. ML predicts alliance ratings from session content and therapist style.
It is important to note that alliance ratings are not always consistent. Moreover, each person's alliance affects the alliance of others. The therapist may perceive the alliance as high when other family members do not. In such cases, the therapist's alliance rating may be higher than the total score of the family.
The results showed that there is a positive correlation between the alliance ratings and the therapeutic progress. If the alliance is high, then therapy will be more successful. Likewise, if the alliance is low, the therapeutic progress will be slower. In this study, the results revealed that the alliance ratings of parents and adolescents are highly related.
In contrast, split alliances in the family were negatively associated with perceived progress. A balanced alliance between adult and youth led to a higher overall alliance rating. A split alliance between adult and youth had significant interaction between them. This interaction was also associated with a lower average rating of the relationship in the next session.
Human coding is used to train ML model
Human coding is the process of recording the content of psychotherapy sessions. In this process, a human coder rates the efficacy of a particular treatment session by coding the content of patient self-report surveys. In addition, it can be used to evaluate adherence to treatment and competence in psychotherapy. Typically, this involves significant resources, including a dedicated team of coders.
ML model predicts process variable from session content
The use of an ML model to predict process variables from session content can be very effective in predicting user behaviour. Studies have shown that ML models are effective predictors of consumers' buying intent after analysing their browsing behaviour. This approach can be very useful in targeting advertisements and making recommendations to consumers in real-time. Providing personalised offers to unregistered consumers is a challenging task.
During the first phase of the modeling process, e-commerce site session logs are collected. These logs store data about the products that are browsed during each session and the start and end timestamps of the session. However, the session logs collected are unstructured and may be missing some of the required information, such as timestamp and session id. These missing data are then removed in a pre-processing step. Next, ML models are evaluated against the sample data. To do this, the dataset is divided into two separate sets, namely, train and test datasets.
The scoring system in this example measures the accuracy of the predictions. A prediction that is less than 0.05 indicates that the customer will not make a purchase. The higher the score, the earlier the purchase is predicted. The scoring system in this case is called a utility score function.
Once a ML model has been trained on a training dataset, it must be evaluated on a test dataset. Then, the model is evaluated against the test dataset to see whether it improves the accuracy of purchase predictions. By analyzing the data, an ML model can identify the most relevant feature in a session. This information can be used to assess the ML model's performance in early purchase prediction.
The proposed EPP framework uses a scoring function to evaluate the performance of an ML model against the test dataset. It identifies ML models that perform better than others in the prediction of early purchase. It also evaluates the utility of an ML model. The higher the utility score, the better the predictive ability of the ML model.