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In emergency departments, effective communication is of utmost importance to ensure the safety of patients. However, communicating can be quite challenging when dealing with high-stress situations. This study aims to assess the efficacy of coaching workshops, informed by a transtheoretical coaching model, in managing communication challenges perceived by emergency nurses and enhancing their communication skills.

The study involved seven emergency room nurses working at a public hospital in Morocco. The data were gathered through various instruments, including observation grids, interviews and pre- and post-test questionnaires.

The study identified prevalent challenges in communication among nurses, notably difficulties in accurately interpreting messages when faced with confrontational attitudes from colleagues or superiors. Additionally, some nurses exhibited asymmetrical communication patterns, prioritizing their own perspectives over others' during interactions. The findings revealed a statistically significant disparity between pre- and post-test scores (P = 0.017). The nurses’ mean score has improved by 5.14 after attending the four workshop coaching experience, passing from 5.71 in the pre-test to 10.85 in the post-test.

This is the first study in Morocco to evaluate the effectiveness of coaching workshops guided by a transtheoretical coaching model in improving communication skills and overcoming communication barriers among working emergency nurses.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.