Knowing what you’re talking about: creating
comprehensive construct definitions and making them
useful in practice
Convenor and Facilitator details:
Gjalt-Jorn Peters (Open University, the Netherlands) and
Rik Crutzen (Maastricht University, the Netherlands) have been reflecting on the methodological state of the art of health psychology and behavior change science for over a decade now. They published a number of papers on the topic, such as
https://doi.org/ggnd9t,
https://doi.org/ggj6wm, and
https://doi.org/ggj8bn. The present workshop supports participants in implementing the implications of “Knowing What We ‘ re Talking About: Facilitating Decentralized, Unequivocal Publication of and Reference to Psychological Construct Definitions and Instructions ” (
https://doi.org/mr4n).
A theory and measurement crisis have been argued to be root causes of psychology ‘ s credibility crisis. In both, the lack of conceptual clarification and the jingle-jangle jungle at the construct definition level as well the measurement level play a central role. One way forward is producing more comprehensive construct definitions that are linked to corresponding instructions for quantitative and qualitative research (
https://doi.org/mr4n).
In this workshop, we will introduce this problem and three open source technical tools: the R package {psyverse}, the Constructor Shiny App, and the PsyCoRe construct repository. Workshop participants will then work on producing a comprehensive construct definition and instructions for working with that construct in practice.
At the end of the workshop, participants will have produced a construct definition with a Unique Construct Identifier, along with instructions for developing measurement instruments for that construct, identifying measurement instruments as measuring that construct in a systematic review, eliciting construct content in qualitative research, and coding qualitative data as informative about the construct.
30min — Interactive presentation to introduce the problem and a solution
30min — Group work (comprehensive construct definitions)
30min — Share experiences and discuss problems and challenges
30min — Group work (construct-specific instructions); coffee/tea break included in this slot
30min — Share experiences and discuss further steps
After this workshop, participants:
… will have been introduced to the problematic state of construct definitions in (health) psychology
… will be familiar with approaches to develop comprehensive construct definitions
… will be familiar with the aforementioned open source tools
… have produced a comprehensive construct definition, attached a Unique Construct Identifier to it, and stored in it PsyCoRe.one
Researchers, practitioners, or policymakers who work with constructs. So, anybody, really.
There is no conflict of interest. Note that the convenors have developed DCTs; however, the standard is completely free and open, and they do not sell it or otherwise profit from it.
The next step: adapting and implementing interventions in a real-world context
Marla Hahnraths and Lisa Harms; CAPHRI, Maastricht University, the Netherlands
Marla Hahnraths: Between 2019-2023, Marla studied the implementation and effectiveness of the scaled-up Healthy Primary School of the Future intervention as part of her PhD project at Maastricht University. Since 2024, she continues her research as assistant professor at Maastricht University, specifically interested in scaling-up, adaptation and implementation of effective school-based interventions. Besides this, she works as an implementation specialist at JOGG, aiming to facilitate the national scaling-up of the Healthy Primary School of the Future.
Lisa Harms: As part of her PhD (2019-2024), Lisa studied the implementation of two lifestyle interventions for children and their parents: SuperFIT and Up for Cooking. During these projects, she had regular contact with implementers in the field, which sparked her interest in navigating fidelity, core components and the adaptation of interventions. Since 2024, Lisa has been working as an assistant professor at Maastricht University, where she aims to further develop these interests.
We are in touch with other researchers in the field of implementation, they might be added after acceptance.
After the workshop, attendees have an increased understanding of implementation theories.
After the workshop, attendees have an increased understanding of adaptation and its related definitions.
After the workshop, attendees can identify the core components of their intervention(s) of interest.
During the workshop, attendees are invited to share their practical experiences, resulting in the formation of a network regarding adaptation and implementation (if desired).
The proposed workshop focusses on the practical implementation of health psychology, specifically concerning the adaptation and implementation of interventions in real-world contexts. The various activities that will be undertaken during the workshop are as follows:
Introductory presentation (1 hour in total)
a. Brief overview of the adaptation and implementation literature (i.e., fidelity vs. adaptation, voltage drop, programme drift, core components, different forms of adaptation) ±30 minutes
b. Application of the theory to two example interventions (SuperFIT and the Healthy Primary School of the Future) ±30 minutes
Interactive component (2 hours in total).
Application of the theory to own case/intervention. We use three thematic tables, with sub-groups (5 participants per group) moving along them
Application of concepts of voltage drop and programme drift to your intervention > identifying potential causes for voltage drop/programme drift
*±5 minutes time to shift*
Identifying core components of your intervention > which components should be maintained during scaling-up/adaptation and why? How can you define your core components?
*±5 minutes time to shift*
Identifying relevant forms of adaptation for your intervention > which forms of adaptation are relevant in your case and why?
Sharing audience’s experiences (30 minutes)
What have you learned? Which lessons/tips would you give to others working on the adaptation/implementation of interventions?
Intended participants have some experience with (the implementation of) interventions in their day-to-day practice (can bring their own case to apply the workshop’s contents to). However, all levels of implementation knowledge are welcome, as we hope to achieve an interactive session.
Maximum number of participants for the workshop:
± 30 participants is the maximum for the workshop. We intend to make use of subgroups, of which the group size will be based on the number of attendees (approximately 5 attendees per subgroup).
Convenors declare they have no conflict of interest concerning the content of the proposed workshop.
Challenges of (open) qualitative research in health psychology
Convenor/Facilitator details:
Gabriela Gore-Gorszewska, PhD; Jagiellonian University, Poland
Gabriela is a clinical psychologist and qualitative researcher in the field of sexology, ageing and health psychology. She gained expertise in collecting and handling sensitive interview-based qualitative data through her involvement in multiple national and international research projects on sexuality, relationships, well-being, body image, activism, and ageing. She publishes on the topic, participates in courses, methodological conferences and trainings related to open science. Gabriela teaches qualitative methods to MA psychology students and works as a psychotherapist.
Qualitative researchers face an increasing pressure to follow the Open Science (OS) postulates, particularly data sharing. However, these requirements are often at odds with the specificity of qualitative epistemology and methods. This workshop aims to equip attendees with practical strategies to navigate the challenging task of balancing the potential benefits of data sharing with the inherent risks. We will address:
Rationale for sharing qualitative datasets: Replicability, transparency, data reuse? Funding agencies and journals requirements versus real-world practice.
Ethical considerations: Reasonable reluctance to share among vulnerable populations and/or when collecting sensitive health information, risks of participants identity being revealed, researchers’ concerns on who and how may reuse the data in the future (academia, media, AI).
Pragmatic challenges: Obtaining truly informed consent in the context of time constraints, limited participant access (technology literacy, health issues), anonymisation process, translation burdens.
Exploration of options: Alternatives to full data sharing and how to responsibly engage with these options?
The workshop aims to empower the attendees to critically assess to what extend and how they can responsibly align with OS postulates. Participants will develop arguments to support their data-sharing choices.
The workshop will feature brief topic introductions, followed by hands-on exercises in small groups and interactive discussions. Participants will be encouraged to reflect on their own research challenges and share insights to enhance learning outcomes.
The workshop is intended for students (MA, PhD) and early-career researchers involved in or planning qualitative research in their line of work.
Maximum number of participants for the workshop:
The maximum number of participants 15.
Convenor declares no conflict of interest.
Bridging AI and qualitative research: a hands-on workshop on AI-driven qualitative analysis for health psychology
Convenor/Facilitator details:
Convenor: Paulina Bondaronek, Felix Naughton
Facilitators: Emma Ward, Sarah Jenner
Facilitators should be experts in the topic and write a short statement about their expertise in this area.
Paulina Bondaronek is a Senior Research Fellow in health psychology and natural language processing, specialising in machine-assisted qualitative analysis for public health and behavioural science. She holds a Wellcome Trust-funded fellowship for her project HUMBLE, a human-centred method for qualitative analysis designed to bring underserved voices and marginalised communities to the surface using AI-based methods while mitigating potential bias in algorithms. Her work critically evaluates AI-generated outputs, exploring their potential to highlight social inequalities while addressing risks such as bias and the reinforcement of existing disparities.
Felix Naughton is Professor of Health Psychology at the University of East Anglia (UEA), UK. He leads a research programme on digital behaviour change interventions, primarily for smoking cessation, integrating Artificial Intelligence for both delivering behavioural support and analysing data. More recently, his work has included investigating resource-saving approaches for analysing ‘Big Qual’ datasets.
Emma Ward is a Research Fellow in the Lifespan Health Research Centre, Norwich Medical School, University of East Anglia. Working mainly in the field of smoking cessation and vaping, she has extensive experience in qualitative methods, including exploratory studies, social media analysis, visual methods, ethnography, intervention development, and trial process evaluation. She has recently been involved in developing a ‘human-in-control’ approach to AI-assisted qualitative analysis and is interested in how AI methods can be optimised while maintaining the core principles of qualitative research.
Sarah Jenner is a Lecturer and qualitative researcher in the School of Health Sciences at the University of Southampton. Her research focuses on engaging young people in meaningful health research to improve their dietary choices. She has also developed and validated a novel method for using large language models (LLMs) to analyse textual data from creative qualitative studies and actively trains researchers on integrating LLMs into qualitative analysis.
Workshop overview abstract:
Recent advancements in Artificial Intelligence (AI) offer exciting possibilities for analysing large-scale qualitative data in health psychology. AI-driven techniques such as topic modelling, deep learning, and large language models (LLMs) allow researchers to identify key themes in text at scale and have the potential to amplify the voices of underrepresented groups, thereby contributing to reducing health inequalities. However, challenges remain in ensuring that AI-generated insights are reliable, meaningful, and free from bias. This workshop will provide a practical and interactive introduction to AI-driven analysis of qualitative data for health psychology researchers. Through hands-on exercises and facilitated discussions, participants will gain experience in interpreting and refining machine-generated textual analyses. They will collaboratively evaluate accuracy, bias, and quality control measures, while exploring strategies for effectively integrating AI into qualitative research.
To introduce key AI methods – including traditional natural language processing techniques, such as topic modelling, as well as deep learning, and LLMs – and their potential applications in health psychology.
To provide hands-on experience in interpreting and refining AI-generated text analysis outputs.
To critically evaluate accuracy, usefulness, nuance and overall quality measures in AI-driven qualitative analysis
To explore best practices and ethical considerations for integrating AI into qualitative health psychology research.
Introduction to AI-driven qualitative analysis in health psychology (45 min)
Hands-on data analysis session: participants will interpret and refine AI-generated text outputs (1.15 hours)
Interactive critical evaluation of AI-driven insights: strengths, limitations, and ethical considerations (1 hour)
Discussion on applications of AI as a tool in health psychology research, incorporating interactive feedback (1 hour)
This workshop is designed for qualitative researchers, health psychologists, and social scientists interested in using AI techniques for large-scale text analysis. No prior coding experience is required. Participants should have an interest in qualitative research methods and AI-assisted approaches, maintaining a healthy scepticism while keeping an open mind to testing and critically evaluating AI outputs.
An indication whether a half-day or full-day workshop is preferred:
The maximum number of participants for the workshop:
Conflict of interest: Facilitators are required to disclose any potential conflict of interest:
