Interdisciplinary multimodal pain therapy (IMPT) is a biopsychosocial treatment approach for patients with chronic pain that comprises at least psychological and physiotherapeutic interventions. Core outcome sets (COSs) are currently developed in different medical fields to standardize and improve the selection of outcome domains, and measurement instruments in clinical trials, to make trial results meaningful, to pool trial results, and to allow indirect comparison between interventions. The objective of this study was to develop a COS of patient-relevant outcome domains for chronic pain in IMPT clinical trials. An international, multiprofessional panel (patient representatives [n = 5], physicians specialized in pain medicine [n = 5], physiotherapists [n = 5], clinical psychologists [n = 5], and methodological researchers [n = 5]) was recruited for a 3-stage consensus study, which consisted of a mixed-method approach comprising an exploratory systematic review, a preparing online survey to identify important outcome domains, a face-to-face consensus meeting to agree on COS domains, and a second online survey (Delphi) establishing agreement on definitions for the domains included. The panel agreed on the following 8 domains to be included into the COS for IMPT: pain intensity, pain frequency, physical activity, emotional wellbeing, satisfaction with social roles and activities, productivity (paid and unpaid, at home and at work, inclusive presentism and absenteeism), health-related quality of life, and patient's perception of treatment goal achievement. The complexity of chronic pain in a biopsychosocial context is reflected in the current recommendation and includes physical, mental, and social outcomes. In a subsequent step, measurement instruments will be identified via systematic reviews.
In Nederland is een instrument nodig om persoonlijk herstel te meten bij mensen met ernstige psychische aandoeningen ten behoeve van Routine Outcome Measuring (ROM). Het doel van het huidige project waarvan verslag wordt gedaan, is om de ervaringen met het gebruik van deze vragenlijst nader te onderzoeken en vast te stellen of het instrument geschikt is gebruik in de praktijk van de GGZ bij ernstige psychische aandoeningen. Presentatie bij de wetenschappelijke symposium ronde II van het 13e Landelijke Phrenos Psychosecongres. Zwolle, Nederland. 23 november 2017.
Critical incident response (CIR) has evolved to require a high level of cultural competence, customization, and adaptability to meet the needs of client organizations while incorporating clinical best practices and current research. The Critical Incident Outcome Measure (CIOM) is a timely and pioneering evidence-based evaluative tool developed by Morneau Shepell over the course of a four-year period. The CIOM tool, based on the Workplace Outcomes Suite (WOS) tool originally developed in 2010, was developed in 2016 [Herlihy et.al., 2018]; beta tests and modifications, along with the publication of a validation paper, were completed in 2017; further feedback was incorporated and an implementation plan developed in 2018; and full program implementation began in 2019.
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The results will be consensus between departments of physiotherapy universities of allied health care about learning outcomes CommunicationThere is no consensus between Dutch Physiotherapy departments on learning outcome of bachelors
In the Netherlands approximately 2 million inhabitants have one or more disabilities. However, just like most people they like to travel and go on holiday.In this project we have explored the customer journey of people with disabilities and their families to understand their challenges and solutions (in preparing) to travel. To get an understanding what ‘all-inclusive’ tourism would mean, this included an analysis of information needs and booking behavior; traveling by train, airplane, boat or car; organizing medical care and; the design of hotels and other accommodations. The outcomes were presented to members of ANVR and NBAV to help them design tourism and hospitality experiences or all.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
