Methods and tools to assess implementation of mental health policies and plans: A systematic review.

Mental health policies and plans (MHPPs) are important policy instruments and powerful tools to facilitate development of mental health systems and services across the world. We aimed to map and analyse methods and tools used to assess the extent, process and impact of implementing MHPPs. We systematically searched peer-reviewed and grey literature across seven scientific databases. We extracted and analysed the data on a) the characteristics of included studies (e.g., policy areas, region of origin, income setting) and b) the methodology and evaluation tools applied to assess the extent and process of implementation. We included 48 studies in the analyses. Twenty-six of these studies employed only qualitative methods (e.g., semi-structured interviews, focus group discussions, desk review, stakeholder consultations); 12 studies used quantitative methods (e.g., trend analysis, survey) and 10 used mixed-methods approaches. Generally, methods and tools used for assessment were described poorly with less than half of the studies providing partial or full details about them. Only three studies provided assessment of full policies. There is a lack of rigorous research to assess implementation MHPPs. Assessments of the implementation of entire MHPPs are almost non-existent. Strategies to assess the implementation of MHPPs should be an integral part of MHPPs.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.

BACKGROUND: Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. OBJECTIVE: This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. METHODS: A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. RESULTS: A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). CONCLUSIONS: These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Mental health system and services in Albania.

AIMS: To describe the mental health system in Albania. METHODS: Data were gathered in 2003 and in 2004 using a new WHO instrument, World Health Organization Assessment Instrument for Mental health Systems (WHO-AIMS), designed for collecting essential information on the mental health system of low and middle income countries. It consists of 6 domains, 28 facets and 156 items. RESULTS: The information collected through WHO AIMS covered the key aspects of mental health system in Albania: the mental health policy and the legislative framework, the network of mental health services and the characteristics of the users, the role of the primary health care, the human resources, the public education and the links with other governmental sectors, monitoring and research. CONCLUSIONS: The data collection through WHO AIMS represented a needed step for a better in-depth knowledge of the system and for implementing actions to strengthen the system. Examples of planned actions were the improvement of the mental health component in primary care, a clear shift of resources from mental hospitals to community facilities, an increase of the outpatient care and an expansion of the mental health information system.

Mental health services in Albania.

Albania, situated in the western Balkans, has an area of 28 748 km2 and a population of 3 069 275 (year 2001), almost one-third of whom are aged 0-14 years. Life expectancy is estimated to be 70.4 years for both sexes (World Health Organization, 2003a). According to the World Health Organization's classification, Albania is a country with low child and low adult mortality rates. The nation's total expenditure on health in 2001 amounted to 3.7% of gross domestic product.