Predicting Psychological Flourishing Among Psychologists: Integrating Self-Compassion, Traditional Regression, and Machine Learning Approaches

Background: Psychological flourishing is a key indicator of optimal mental health and professional well-being, particularly among psychologists who are routinely exposed to emotionally demanding clinical environments. Although self-compassion has consistently been associated with positive psychological outcomes, few studies have integrated traditional statistical methods with machine learning approaches to predict psychological flourishing among psychologists. Objective: This study aimed to examine the relationship between self-compassion and psychological flourishing among psychologists in Saudi Arabia, identify the unique contribution of self-compassion dimensions, evaluate the predictive performance of supervised machine learning models, and compare their performance with traditional multiple linear regression. Methods: A cross-sectional correlational design was employed, involving 224 psychologists practicing in Saudi Arabia. Participants completed the Self-Compassion Scale and the Flourishing Scale. Descriptive statistics, Pearson’s correlation, and multiple linear regression analyses were performed using IBM SPSS Statistics version 29.0. In addition, Random Forest Regression and Support Vector Regression (SVR) models were implemented in Python using scikit-learn version 1.8.0 to predict psychological flourishing based on self-compassion dimensions together with demographic and professional characteristics. Model performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Results: Overall, self-compassion was positively associated with psychological flourishing (r = 0.627, p < 0.001). Multiple linear regression showed that self-kindness had a significant positive independent association with psychological flourishing (β = 0.292, p = 0.001), whereas over-identification had a significant negative independent association (β = −0.206, p = 0.009). The regression model explained 40.5% of the variance in psychological flourishing (R2 = 0.405, p < 0.001). In the held-out test-set comparison using the same predictor set, predictive performance was similar across multiple linear regression (R2 = 0.273; RMSE = 3.583; MAE = 2.849), Random Forest Regression (R2 = 0.282; RMSE = 3.562; MAE = 2.771), and Support Vector Regression (R2 = 0.272; RMSE = 3.587; MAE = 2.768), with no substantial predictive advantage of the machine-learning models over the linear benchmark. Conclusions: Self-compassion, particularly self-kindness and over-identification, was significantly correlated with psychological flourishing among psychologists. Machine-learning models demonstrated predictive performance comparable to traditional regression, with no substantial predictive advantage over the linear benchmark, indicating that increased model complexity did not improve out-of-sample prediction in the present sample.