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Those who care for people with schizophrenia and substance use disorders (PLS-SUD) are faced with the complex demands of a long journey to recovery. For the carers, this translates into specific needs related to various areas of their lives. However, few studies have contributed to the understanding of these carers’ needs. The purpose of this qualitative evaluative study is to identify, understand and prioritize the needs of PLS-SUD carers in the context of intervention design from the viewpoint of carers themselves (n = 9), those they were accompanying (n = 5) and other key actors involved (n = 10).

A design of action research was employed. Data analysis was done in three phases: concept map analysis, thematic analysis and transversal analysis of the results from two focus groups, 28 interview transcriptions and a logbook.

Over 60 needs were identified. After review, 39 of those were selected for prioritization. For needs related to the carers’ role as clients of the health-care system, the committee prioritized the needs for support, sharing with other carers and improving their own well-being. For the role of supporter, knowledge about substance use disorders and their interactions with psychotic disorders as well as skills such as communication and problem resolution were considered priorities. Needs to be prioritized relating to the role of partner were fewer.

The results of this study highlight the diversity and complexity of the needs experienced by carers.

This is among the first needs surveys carried out by stakeholders to describe the needs of PLS-SUD carers.

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.

Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.

This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.

In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.

Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.