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The purpose of this paper is to present the results of a scoping review on the implementation of Article12 in health care. The scoping review will provide a summary and overview of the key concepts and published literature on this topic internationally. Article 12 of the United Nations Convention on the Rights of the Child (1989) states that children have a right to express their views, to have them heard and for their views to be given due weight in all matters that affect them. Despite increased calls for Article 12 to be given attention in health care, there is little evidence to suggest this has been well implemented and embedded in Australian health-care delivery. The scoping review was undertaken to provide a summary and overview of the key concepts and published literature on this topic internationally.

A five-step methodological framework described by Arksey and O’Malley (2005) was used to undertake the scoping review. Preferred Reporting Items for Systematic Reviews and Meta-Analysis was used as a guideline for undertaking the study selection.

Children are still not routinely involved in health-care decision-making, are frequently left out of service planning and evaluation and the perception that they lack the capability to make rational decisions persists.

While there has been a focus on research that investigates children’s participation in health-care decision-making in recent years, there is little that directs attention specifically to the implementation of Article 12, particularly in Australian health care. Recommendations are made for further research in these areas.

Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.