Use of the terms "Wellbeing" and "Quality of Life" in health sciences: A conceptual framework

Background and Objectives: The assessment of wellbeing is a top priority in health sciences. The aim of this paper is to review the history of the concept of wellbeing and “Quality of Life” (QoL), and to understand the theories and assumptions that guided this field in order to provide a conceptual framework that may eventually facilitate the development of a formal synset (grouping of synonyms and semantically similar terms) of health-related wellbeing Methods: The history of the concept of wellbeing and QoL was reviewed in order to provide a conceptual framework. Results: Huge differences exist on the definition of “Wellbeing” and its relationship with QoL, “Happiness” and “Functioning” in the health context. From a dimensional perspective, health related wellbeing could be regarded as an overarching construct characterised by asymmetrical polarity, where “wellbeing” embeds the concept of “ill-being” as “health” incorporates de concept of “disease”. Conclusions: A common conceptual framework of these terms may eventually facilitate the development of a formal synset of health-related wellbeing. This terminological clarification should be part of a new taxonomy of health-related wellbeing based on the International Classification of Functioning, Disability and Health (ICF) framework that may facilitate knowledge transfer across different sectors and semantic interoperability for care management and planningThe research leading to these results has received funding from the European Community’s Seventh Framework Programme under grant agreement numbers 223071 (COURAGE in Europe) and 282586 (ROAMER), from the Instituto de Salud Carlos III-FIS research grant number PS09/00295, and from the Spanish Ministry of Science and Innovation ACI-Promociona (ACI2009-1010 and ACI- 2011-1080). The study was supported by the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos II

Análisis del lenguaje en grupos de apoyo en Internet de salud mental

Assisting moderators to triage critical posts in Internet Support Groups is relevant to ensure its safe use. Automated text classification methods analysing the language expressed in posts of online forums is a promising solution. Natural Language Processing and Machine Learning technologies were used to build a triage post classifier using a dataset from Reachout.com mental health forum. When comparing with the state-of-the-art, our solution achieved the best classification performance for the crisis posts (52%), which is the most severe class.Dar asistencia a los moderadores de Grupos de Ayuda en Internet es importante para asegurar su uso de forma segura. Métodos de clasificación de textos que analizan el lenguaje utilizado en estos forums es una de las posibles soluciones. Esta investigación trata de utilizar tecnologías del procesamiento del lenguaje natural y el aprendizaje automático para construir un sistema de clasificación de triaje usando datos del forum de salud mental Reachout.com. Al comparar con el estado de la cuestión, nuestra propuesta alcanza el mejor rendimiento para la clase crisis (52%), siendo ésta la de mayor importancia

The brain injury case management taxonomy (BICM-T):a classification of community-based case management interventions for a common language

AbstractBackgroundCase management is a complex intervention. Complexity arises from the interaction of different components: the model (theoretical basis), implementation context (service), population and health condition, focus for the intervention (client and/or their family), case manager's actions (interventions) and the target of case management (integrated care and support, client's community participation). There is a lack of understanding and a common language. To our knowledge there is no classification (taxonomy) for community-based case management.ObjectiveTo develop a community-based case management in brain injury taxonomy (BICM-T), as a common language and understanding of case management for use in quality analysis, policy, planning and practice.MethodsThe mixed qualitative methods used multiple sources of knowledge including scoping, framing and a nominal group technique to iteratively develop the Beta version (draft) of the taxonomy. A two part developmental evaluation involving case studies and mapping to international frameworks assessed the applicability and acceptability (feasibility) before finalization of the BICM-T.ResultsThe BICM-T includes a definition of community-based case management, taxonomy trees, tables and a glossary. The interventions domain tree has 9 main actions (parent category): engagement, holistic assessment, planning, education, training and skills development, emotional and motivational support, advising, coordination, monitoring; 17 linked actions (children category); 8 related actions; 63 relevant terms defined in the glossary.ConclusionsThe BICM-T provides a knowledge map with the definitions and relationships between the core actions (interventions domain). Use of the taxonomy as a common language will benefit practice, quality analysis, evaluation, policy, planning and resource allocation

Fit for Purpose—Re-Designing Australia’s Mental Health Information System

Background: Monitoring and reporting mental health is complex. Australia's first National Mental Health Strategy in 1992 included a new national commitment to accountability and data collection in mental health. This article provides a narrative review of thirty years of experience. Materials and Methods: This review considers key documents, policies, plans and strategies in relation to the evolution of mental health data and reporting. Documents produced by the Federal and the eight state and territory governments are considered, as well as publications produced by key information agencies, statutory authorities and others. A review of this literature demonstrates both its abundance and limitations. Results: Australia's approach to mental health reporting is characterised by duplication and a lack of clarity. The data available fail to do justice to the mental health services provided in Australia. Mental health data collection and reporting processes are centrally driven, top-down and activity-focused, largely eschewing actual health outcomes, the social determinants of mental health. There is little, if any, link to clearly identifiable service user or carer priorities. Consequently, it is difficult to link this process longitudinally to clinical or systemic quality improvement. Initial links between the focus of national reform efforts and mental health data collection were evident, but these links have weakened over time. Changes to governance and reporting, including under COVID, have made the task of delivering accountability for mental health more difficult. Conclusion: Australia's current approach is not fit for purpose. It is at a pivotal point in mental health reform, with new capacity to use modelled data to simulate prospective mental health reform options. By drawing on these new techniques and learning the lessons of the past, Australia (and other nations) can design and implement more effective systems of planning, reporting and accountability for mental health