Empathy plays a crucial role in psychosocial and psychological interventions, greatly impacting rapport building, patient adherence, and satisfaction with treatment. Empathetic interactions enhance patient’s self-reflection and the delivery of more personalized therapeutic interventions tailored to the unique needs of each patient, thereby improving the overall quality of care. Despite empathy being central to psychosocial interventions, there are currently no valid and reliable patient-centered tools that assess the lay-therapist empathy that they show and/or exhibit toward their patients.

In this study, the patient-rated Empathy Scale for Lay Therapists was developed to assess empathy in community health workers delivering psychosocial interventions. Psychometric validation was based on a cross-sectional study embedded in a non-inferiority cluster randomized trial of the Thinking Healthy Programme for perinatal depression in Pakistan.

Community testing with perinatal women confirmed the scale’s understandability and logical structure, highlighting its face validity. Among the 980 trial participants, a high level of agreement with the Empathy Scale for Lay Therapists (mean score 2.616) was observed, indicating effective communication and empathy from health workers. The scale demonstrated excellent internal consistency (Cronbach’s alpha 0.96). Exploratory Factor Analysis revealed a unidimensional structure, capturing 87.81% of the total variance, with strong factor loadings.

Education can be viewed as a control theory problem in which students seek ongoing exogenous input—either through traditional classroom teaching or other alternative training resources—to minimize the discrepancies between their actual and target (reference) performance levels. Using illustrative data from $n=784$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$n=784$$\end{document} Dutch elementary school students as measured using the Math Garden, a web-based computer adaptive practice and monitoring system, we simulate and evaluate the outcomes of using off-line and finite memory linear quadratic controllers with constraintsto forecast students’ optimal training durations. By integrating population standards with each student’s own latent change information, we demonstrate that adoption of the control theory-guided, person- and time-specific training dosages could yield increased training benefits at reduced costs compared to students’ actual observed training durations, and a fixed-duration training scheme. The control theory approach also outperforms a linear scheme that provides training recommendations based on observed scores under noisy and the presence of missing data. Design-related issues such as ways to determine the penalty cost of input administration and the size of the control horizon window are addressed through a series of illustrative and empirically (Math Garden) motivated simulations.