title: Enhancing consumer health question answering systems through rhetorical structure theory-guided large language model

authors: Liu, Chen

abstract: Consumer Health Question Answering (CHQA) systems have traditionally been designed around isolated capabilities—either factual medical correctness or empathetic support—rather than delivering integrated, multi-dimensional assistance. This separation fails to reflect the reality of consumer health consultations, where individuals typically express intertwined medical concerns, personal circumstances, and emotional distress within a single query. Existing CHQA architectures, often optimised for short, single-intent inputs, prioritise answer generation over question understanding and lack explicit mechanisms to jointly interpret informational and emotional support needs. Although Large Language Models (LLMs) have advanced the state of medical QA, most LLM-based systems still struggle to reconcile clinical reliability with context-sensitive empathy in complex consumer health questions.

To address this gap, this study adopts a Design Science Research (DSR) paradigm and introduces Joint Medical–Emotional Question Answering (JMEQA) as a new task that jointly understand and response medical informational needs and emotional support needs in Consumer Health Questions (CHQs). Grounded in Rhetorical Structure Theory (RST) and Appraisal Framework for Clinical Empathy (AFCE), the research proposes the Med-Emo CHQA architecture, a neural-symbolic unified, LLM-based system that augments answer generation with explicit question understanding. The architecture integrates hierarchical modules for question understanding and answer generation, unifying symbolic knowledge (an RST-based discourse structure tree that encodes intents, contexts, and rhetorical relations during the question-understanding stage) with neural network (LLM) via supervised fine-tuning and structured prompting.

To support system training and evaluation, this study constructs CHQA-MedEmo, the first large-scale, multi-layer corpus of Chinese health consultation records. The corpus is annotated for both medical informational needs and emotional support needs, with explicit contextual spans and discourse relations. System performance is assessed through controlled experiments and an expert reader study using a human-centred evaluation framework that jointly measures accuracy, personalisation, and empathy, alongside latency. Experimental and reader-study results show that the proposed architecture outperforms baseline and ablated models in full medical-need coverage and empathetic alignment, while maintaining lower latency.

Overall, this study advances the design of CHQA systems by demonstrating how symbolic, discourse-aware question understanding can be embedded into LLM workflows to produce responses that are simultaneously medically reliable, personally tailored, and emotionally supportive. The findings contribute design knowledge for Neural-Symbolic Integration (NSI), LLM-based CHQA in online health consultation settings and offer a scalable architectural paradigm for building complex, human-centred AI systems that balance informational rigour with affective care.

text: Enhancing consumer health question answering systems through rhetorical structure theory-guided large language model Consumer Health Question Answering (CHQA) systems have traditionally been designed around isolated capabilities—either factual medical correctness or empathetic support—rather than delivering integrated, multi-dimensional assistance. This separation fails to reflect the reality of consumer health consultations, where individuals typically express intertwined medical concerns, personal circumstances, and emotional distress within a single query. Existing CHQA architectures, often optimised for short, single-intent inputs, prioritise answer generation over question understanding and lack explicit mechanisms to jointly interpret informational and emotional support needs. Although Large Language Models (LLMs) have advanced the state of medical QA, most LLM-based systems still struggle to reconcile clinical reliability with context-sensitive empathy in complex consumer health questions.

To address this gap, this study adopts a Design Science Research (DSR) paradigm and introduces Joint Medical–Emotional Question Answering (JMEQA) as a new task that jointly understand and response medical informational needs and emotional support needs in Consumer Health Questions (CHQs). Grounded in Rhetorical Structure Theory (RST) and Appraisal Framework for Clinical Empathy (AFCE), the research proposes the Med-Emo CHQA architecture, a neural-symbolic unified, LLM-based system that augments answer generation with explicit question understanding. The architecture integrates hierarchical modules for question understanding and answer generation, unifying symbolic knowledge (an RST-based discourse structure tree that encodes intents, contexts, and rhetorical relations during the question-understanding stage) with neural network (LLM) via supervised fine-tuning and structured prompting.

To support system training and evaluation, this study constructs CHQA-MedEmo, the first large-scale, multi-layer corpus of Chinese health consultation records. The corpus is annotated for both medical informational needs and emotional support needs, with explicit contextual spans and discourse relations. System performance is assessed through controlled experiments and an expert reader study using a human-centred evaluation framework that jointly measures accuracy, personalisation, and empathy, alongside latency. Experimental and reader-study results show that the proposed architecture outperforms baseline and ablated models in full medical-need coverage and empathetic alignment, while maintaining lower latency.

Overall, this study advances the design of CHQA systems by demonstrating how symbolic, discourse-aware question understanding can be embedded into LLM workflows to produce responses that are simultaneously medically reliable, personally tailored, and emotionally supportive. The findings contribute design knowledge for Neural-Symbolic Integration (NSI), LLM-based CHQA in online health consultation settings and offer a scalable architectural paradigm for building complex, human-centred AI systems that balance informational rigour with affective care.

year: 2026-03-23

title: Industrial and organisational psychology

authors: O’Driscoll, Michael P.

subjects: psychology

abstract: Industrial and organisational (I/0) psychology is concerned with people’s work-related values, attitudes and behaviours, and how these are influenced by the conditions in which they work. I/O psychologists contribute to both the effectiveness of organisations (e.g. improving productivity) and the health and well-being of people working within organisations. The field is related to other disciplines, such as organisational behaviour and human resource management, and also has close links with other sub-disciplines within psychology, especially social psychology and some aspects of human experimental psychology (e.g. cognition).

text: Industrial and organisational psychology Industrial and organisational (I/0) psychology is concerned with people’s work-related values, attitudes and behaviours, and how these are influenced by the conditions in which they work. I/O psychologists contribute to both the effectiveness of organisations (e.g. improving productivity) and the health and well-being of people working within organisations. The field is related to other disciplines, such as organisational behaviour and human resource management, and also has close links with other sub-disciplines within psychology, especially social psychology and some aspects of human experimental psychology (e.g. cognition).

year: 2007

title: He Pikinga Waiora Implementation Framework: A tool for chronic disease intervention effectiveness in Maori and other indigenous communities

authors: Oetzel, John G.

subjects: Science & Technology

abstract: Introduction: Health outcomes for Māori are significantly worse than non-Maori in New Zealand; these inequities mirror those found in indigenous communities elsewhere. Evidence-based interventions with established efficacy may not be effective in indigenous communities without addressing specific implementation challenges. We present an implementation framework for chronic condition interventions for Māori.

Theory/Methods: The He Pikinga Waiora Implementation Framework has indigenous self-determination at its core and consists of four elements: cultural-centeredness, community engagement, systems thinking, and integrated knowledge translation.  All elements have demonstrated evidence of positive implementation outcomes.  A coding scheme derived from the Framework was applied to 13 studies of diabetes prevention in indigenous communities from a systematic review.

Results: Cross-tabulations demonstrated that culture-centeredness (p=.008) and community engagement (p=.009) explained differences in diabetes outcomes and community engagement (p=.098) explained difference in blood pressure outcomes.

Discussion and Conclusions: The He Pikinga Waiora Implementation Framework is well suited to advance implementation science for Māori and other indigenous communities. The framework has promise as a policy and planning tool to evaluate and design effective interventions for chronic disease prevention.

Lessons Learned: We needed to revise the coding framework in order to make the implication framework concrete.

Limitations: This study has a limitation in that we coded information about interventions from the published articles and not the interventions themselves. Additionally, the study only included 13 interventions.

Suggestions for Future Direction: Further research should provide stronger evidence of the usefulness of the framework particularly with Māori end-users. There will also be a need to understand the differential contribution of each of the four elements to health outcomes.

text: He Pikinga Waiora Implementation Framework: A tool for chronic disease intervention effectiveness in Maori and other indigenous communities Introduction: Health outcomes for Māori are significantly worse than non-Maori in New Zealand; these inequities mirror those found in indigenous communities elsewhere. Evidence-based interventions with established efficacy may not be effective in indigenous communities without addressing specific implementation challenges. We present an implementation framework for chronic condition interventions for Māori.

Theory/Methods: The He Pikinga Waiora Implementation Framework has indigenous self-determination at its core and consists of four elements: cultural-centeredness, community engagement, systems thinking, and integrated knowledge translation.  All elements have demonstrated evidence of positive implementation outcomes.  A coding scheme derived from the Framework was applied to 13 studies of diabetes prevention in indigenous communities from a systematic review.

Results: Cross-tabulations demonstrated that culture-centeredness (p=.008) and community engagement (p=.009) explained differences in diabetes outcomes and community engagement (p=.098) explained difference in blood pressure outcomes.

Discussion and Conclusions: The He Pikinga Waiora Implementation Framework is well suited to advance implementation science for Māori and other indigenous communities. The framework has promise as a policy and planning tool to evaluate and design effective interventions for chronic disease prevention.

Lessons Learned: We needed to revise the coding framework in order to make the implication framework concrete.

Limitations: This study has a limitation in that we coded information about interventions from the published articles and not the interventions themselves. Additionally, the study only included 13 interventions.

Suggestions for Future Direction: Further research should provide stronger evidence of the usefulness of the framework particularly with Māori end-users. There will also be a need to understand the differential contribution of each of the four elements to health outcomes.

year: 2018