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: The audacity of Trump: How he won and what we missed

authors: Steff, Reuben

abstract: Donald Trump made a number of key strategic decisions during his election campaign. Contrary to the common view that his campaign was marked by incompetence and dysfunction, there is considerable evidence that he made adroit decisions at key times. These were overlooked and dismissed by his political opponents to their ultimate disadvantage and defeat. If the Democratic Party hopes to take back the White House in four years' time, and the wider world wants to understand the 'Trump phenomenon', it is imperative that we look at the logical thread that underlies Trump's seemingly irrational behaviour.

text: The audacity of Trump: How he won and what we missed Donald Trump made a number of key strategic decisions during his election campaign. Contrary to the common view that his campaign was marked by incompetence and dysfunction, there is considerable evidence that he made adroit decisions at key times. These were overlooked and dismissed by his political opponents to their ultimate disadvantage and defeat. If the Democratic Party hopes to take back the White House in four years' time, and the wider world wants to understand the 'Trump phenomenon', it is imperative that we look at the logical thread that underlies Trump's seemingly irrational behaviour.

year: 2017

title: The Aroha block to 1879

authors: Hart, Philip

abstract: Te Aroha was valued by Maori because of its strategic position as well as its healing hot springs, while Pakeha anticipated acquiring a large area (which shrank in size once surveyed) of fertile farming land. Before the Crown could acquire the block, the ownership of a disputed area fought over by the Marutuahu tribes (especially Ngati Maru) and Ngati Haua had to be determined. After the battle of Taumatawiwi of 1830, claimed as a victory by both sides, subsequent smaller battles continued for some years. In place of deciding ownership on this basis, the new land court struggled to make a judgment on the basis of  contradictory claims about ancestry, battles large and small, and occupation.
One part of the larger Aroha block, Ruakaka, was considered separately in 1869, resulting in the court allocating it to Ngati Haua. Also in that year, the first hearing of the Aroha Block was held, at Matamata, with the same outcome. Because of Maratuahu anger and threats of violence, Ngati Haua failed to survey the land as required, and a second hearing was held in Auckland, in 1871, at which Ngati Maru triumphed. Subsequently the block was occupied, but conflicts continued, notably when Ngati Haua gave a Pakeha permission to run cattle at Waiharakeke, further upriver. 
Starting in 1873, James Mackay slowly purchased the interests of individuals claiming ownership of the block, and paid off the Ngati Haua claimants. Then in December 1876, Ngati Tumutumu/Ngati Rahiri claimed the land for themselves, and in the following year the ownership of the Waitoki block, downstream from Te Aroha, was contested. In July 1878, a third hearing of the Aroha case commenced, resulting in Ngati Rahiri being allocated £3,000 and 7,500 acres, the remainder of the block going to Ngati Maru. Subsequently, the process of subdividing the owners’ interests proceeded, very slowly, and a small number of Pakeha settled and commenced farming or storekeeping.

text: The Aroha block to 1879 Te Aroha was valued by Maori because of its strategic position as well as its healing hot springs, while Pakeha anticipated acquiring a large area (which shrank in size once surveyed) of fertile farming land. Before the Crown could acquire the block, the ownership of a disputed area fought over by the Marutuahu tribes (especially Ngati Maru) and Ngati Haua had to be determined. After the battle of Taumatawiwi of 1830, claimed as a victory by both sides, subsequent smaller battles continued for some years. In place of deciding ownership on this basis, the new land court struggled to make a judgment on the basis of  contradictory claims about ancestry, battles large and small, and occupation.
One part of the larger Aroha block, Ruakaka, was considered separately in 1869, resulting in the court allocating it to Ngati Haua. Also in that year, the first hearing of the Aroha Block was held, at Matamata, with the same outcome. Because of Maratuahu anger and threats of violence, Ngati Haua failed to survey the land as required, and a second hearing was held in Auckland, in 1871, at which Ngati Maru triumphed. Subsequently the block was occupied, but conflicts continued, notably when Ngati Haua gave a Pakeha permission to run cattle at Waiharakeke, further upriver. 
Starting in 1873, James Mackay slowly purchased the interests of individuals claiming ownership of the block, and paid off the Ngati Haua claimants. Then in December 1876, Ngati Tumutumu/Ngati Rahiri claimed the land for themselves, and in the following year the ownership of the Waitoki block, downstream from Te Aroha, was contested. In July 1878, a third hearing of the Aroha case commenced, resulting in Ngati Rahiri being allocated £3,000 and 7,500 acres, the remainder of the block going to Ngati Maru. Subsequently, the process of subdividing the owners’ interests proceeded, very slowly, and a small number of Pakeha settled and commenced farming or storekeeping.

year: 2016