Putting "the broken bits together": A qualitative exploration of the impact of communication changes in dementia.

INTRODUCTION: Communication is an area of health and functioning that is profoundly affected by dementia. While it is known that people living with dementia and their care partners experience disruptions to daily activities and social engagement, detailed knowledge about the lived impact of dementia-related communication changes is lacking. This study sought an in-depth understanding of the lived experience of dementia-related communication changes and the associated impact, needs, and strategies. METHODS: As part of an overarching participatory design study, a qualitative (interpretive description) exploration was undertaken with people living with dementia and their care partners. Data from semi-structured interviews were analysed using thematic analysis. RESULTS: We interviewed 13 people living with dementia and 21 care partners and drew three themes and 10 subthemes from the interviews. The first theme illustrates how dementia changes communication which in turn changes life; the second captures the impact of changes on people living with dementia and care partners emotionally and in the context of relationships; and the third describes some positive and constructive ways of moving forward with dementia-related communication changes. CONCLUSIONS: Interventions to enhance function, participation, and wellbeing for people living with dementia and their care partners need to encompass support for communication changes. There is a need to ensure that people living with dementia feel dignified and respected during communication, and that care partners and inclusive communities are educated, trained, and supported to facilitate communication.

An Extension of Combinatorial Contextuality for Cognitive Protocols.

This article extends the combinatorial approach to support the determination of contextuality amidst causal influences. Contextuality is an active field of study in Quantum Cognition, in systems relating to mental phenomena, such as concepts in human memory. In the cognitive field of study, a contemporary challenge facing the determination of whether a phenomenon is contextual has been the identification and management of disturbances. Whether or not said disturbances are identified through the modeling approach, constitute causal influences, or are disregardableas as noise is important, as contextuality cannot be adequately determined in the presence of causal influences. To address this challenge, we first provide a formalization of necessary elements of the combinatorial approach within the language of canonical causal models. Through this formalization, we extend the combinatorial approach to support a measurement and treatment of disturbance, and offer techniques to separately distinguish noise and causal influences. Thereafter, we develop a protocol through which these elements may be represented within a cognitive experiment. As human cognition seems rife with causal influences, cognitive modelers may apply the extended combinatorial approach to practically determine the contextuality of cognitive phenomena.

Personal and complex: The needs and experiences related to technology use for people living with dementia.

BACKGROUND: With increasing focus on living well with dementia, technology has been identified as having potential benefits for safety, independence and wellbeing. Despite a large growth in specialised technology development, there has been limited uptake. There is a need to understand the current use and needs related to technology for people living with dementia and their care partners (informal carers). METHODS: As part of a participatory design study, a qualitative inquiry into technology experiences and needs was undertaken within an interpretive description approach. A cross-disciplinary team including living experience experts (people living with dementia, unpaid care partners) was involved. Semi-structured interviews, including sharing technology locations and supports, were conducted, audio-recorded and transcribed. Key aspects of the technology use experience were constructed. FINDINGS: Thirteen people living with dementia and 21 care partners participated in the study. Two core aspects of technology use were identified: Lots of moving parts: the complex context, and A technology cycle: the use and non-use. The findings indicated that the context involved an individual configuration of multiple factors including the user, their technology identity, their supporters, the technology and the environment. The experience is underpinned by shifting foundations created by constantly changing technologies and user characteristics. The cycle of use and non-use identified the processes required to maintain technology engagement. CONCLUSION: The use of technology for people living with dementia is complex and personal. Future technology development, policies and practices need to consider this complexity and the effort required to keep using technology to realise the benefits.

Tracking conflict and emotions with a computational qualitative discourse analytic support approach.

Accurate inferences of the emotional state of conversation participants can be critical in shaping analysis and interpretation of conversational exchanges. In qualitative analyses of discourse, most labelling of the perceived emotional state of conversation participants is performed by hand, and is limited to selected moments where an analyst may believe that emotional information is valuable for interpretation. This reliance on manual labelling processes can have implications for repeatability and objectivity, both in terms of accuracy, but also in terms of changes in emotional state that might go unnoticed. In this paper we introduce a qualitative discourse analytic support method intended to support the labelling of emotional state of conversational participants over time. We demonstrate the utility of the technique using a suite of well-studied broadcast interviews, taking a particular focus on identifying instances of inter-speaker conflict. Our findings indicate that this two-step machine learning approach can help decode how moments of conflict arise, sustain, and are resolved through the mapping of emotion over time. We show how such a method can provide useful evidence of the change in emotional state by interlocutors which could be useful to prompt and support further in-depth study.

An Automated Approach to Examining Pausing in the Speech of People With Dementia.

Dementia is a common neurodegenerative condition involving the deterioration of cognitive and communication skills. Pausing in the speech of people with dementia is a dysfluency that may be used to signal conversational trouble in social interaction. This study aimed to examine the speech-pausing profile within picture description samples from people with dementia and healthy controls (HCs) within the DementiaBank database using the Calpy computational speech processing toolkit. Sixty English-speaking participants between the ages of 53 and 88 years (Mage = 67.43, SD = 8.33; 42 females) were included in the study: 20 participants with mild cognitive impairment, 20 participants with moderate cognitive impairment, and 20 HCs. Quantitative analysis shows a progressive increase in the duration of pausing between HCs, the mild dementia group, and the moderate dementia group, respectively.

The Promotion of Policy Changes Restricting Access to Codeine Medicines on Twitter: What do National Pain Organizations Say?

Codeine is one of the most common opioid medicines for treating pain. Australia introduced policy changes in February 2018 to up-schedule codeine to prescription-only medicine due to concerns of adverse effects, opioid dependency, and overdose-related mortality. This study investigated the frequency and content of messages promoted on Twitter by 4 Australian peak pain organizations, pre- and postpolicy implementation. A time series analysis examined frequency of Twitter posts over a 48-week period. Text analysis via Leximancer examined message content. Results showed that promotion and education of the pending policy change dominated the Twitter feed prior to up-scheduling. However, immediately following policy change, there was a shift in content towards promoting conferences and research, and a significant decrease in the frequency of codeine-related posts, compared to opioid-related non-codeine posts. The findings suggest that pain organizations can provide timely and educational policy dissemination in the online environment. They have implications for individuals with chronic pain who use the Internet for health information and the degree to which they can trust these sources, as well as health professionals. Further research is required to determine if public health campaigns can be targeted to prevent opioid-related harm and improve pain care via this increasingly used medium. PERSPECTIVE: This study presents a first look at what information is being communicated by influential pain organizations that have an online Twitter presence and how messages were delivered during a major policy change restricting access to codeine medicines. Insights could drive targeted future online health campaigns for improved pain management.

Toward the Development of SMART Communication Technology: Automating the Analysis of Communicative Trouble and Repair in Dementia.

BACKGROUND AND OBJECTIVES: Communication difficulties have been reported as one of the most stress-inducing aspects of caring for people with dementia. Notably, with disease progression comes an increase in the frequency of communication difficulty and a reduction in the effectiveness of attempts to remedy breakdowns in communication. The aim of the current research was to evaluate the utility of an automated discourse analysis tool (i.e., Discursis) in distinguishing between different types of trouble and repair signaling behaviors, demonstrated within conversations between people with dementia and their professional care staff. RESEARCH DESIGN AND METHODS: Twenty conversations between people with dementia and their professional care staff were human-coded for instances of interactive/noninteractive trouble and typical/facilitative repair behaviors. Associations were then examined between these behaviors and recurrence metrics generated by Discursis. RESULTS: Significant associations were identified between Discursis metrics, trouble-indicating, and repair behaviors. DISCUSSION AND IMPLICATIONS: These results suggest that discourse analysis software is capable of discriminating between different types of trouble and repair signaling behavior, on the basis of term recurrence calculated across speaker turns. The subsequent recurrence metrics generated by Discursis offer a means of automating the analysis of episodes of conversational trouble and repair. This achievement represents the first step toward the future development of an intelligent assistant that can analyze conversations in real time and offers support to people with dementia and their carers during periods of communicative trouble.

Social semantic networks: Measuring topic management in discourse using a pyramid of conceptual recurrence metrics.

Studies of conversational encounters typically employ manual discourse analysis methods to reveal participants' topic management patterns, usually focusing on turn-by-turn interactions within a specific social context. These analyses, while powerful, are time-consuming to apply and can prove difficult to generalize. Recurrence analysis has recently been applied to discourse datasets to model how individual terms and concepts recur over whole conversation time scales and relate patterns of recurrence to topic management practices by individuals. In this paper, we propose a new multi-level quantitative method for modelling the topical interaction dynamics in conversation based on conceptual recurrence quantification analysis. The new protocol develops a hierarchy of speakers and their interactions, and partitions recurrence based on these groups. The new protocol is evaluated against expert human coding of television broadcast interviews. Our analysis reveals topic use patterns and networks of conceptual engagement (person-person and group-group) that show experts preferentially engaging with other experts rather than with laypeople, findings that are consistent with prior expectations for this discourse, although never before expressed as metrics. The studies provide a starting point for new computational protocols to provide fast, semi-automated methods for measuring the degree of conceptual interaction between individuals and groups.

Using Discursis to enhance the qualitative analysis of hospital pharmacist-patient interactions.

INTRODUCTION: Pharmacist-patient communication during medication counselling has been successfully investigated using Communication Accommodation Theory (CAT). Communication researchers in other healthcare professions have utilised Discursis software as an adjunct to their manual qualitative analysis processes. Discursis provides a visual, chronological representation of communication exchanges and identifies patterns of interactant engagement. AIM: The aim of this study was to describe how Discursis software was used to enhance previously conducted qualitative analysis of pharmacist-patient interactions (by visualising pharmacist-patient speech patterns, episodes of engagement, and identifying CAT strategies employed by pharmacists within these episodes). METHODS: Visual plots from 48 transcribed audio recordings of pharmacist-patient exchanges were generated by Discursis. Representative plots were selected to show moderate-high and low- level speaker engagement. Details of engagement were investigated for pharmacist application of CAT strategies (approximation, interpretability, discourse management, emotional expression, and interpersonal control). RESULTS: Discursis plots allowed for identification of distinct patterns occurring within pharmacist-patient exchanges. Moderate-high pharmacist-patient engagement was characterised by multiple off-diagonal squares while alternating single coloured squares depicted low engagement. Engagement episodes were associated with multiple CAT strategies such as discourse management (open-ended questions). Patterns reflecting pharmacist or patient speaker dominance were dependant on clinical setting. DISCUSSION AND CONCLUSIONS: Discursis analysis of pharmacist-patient interactions, a novel application of the technology in health communication, was found to be an effective visualisation tool to pin-point episodes for CAT analysis. Discursis has numerous practical and theoretical applications for future health communication research and training. Researchers can use the software to support qualitative analysis where large data sets can be quickly reviewed to identify key areas for concentrated analysis. Because Discursis plots are easily generated from audio recorded transcripts, they are conducive as teaching tools for both students and practitioners to assess and develop their communication skills.

Online self-expression and experimentation as 'reflectivism': Using text analytics to examine the participatory forum Hello Sunday Morning.

Hello Sunday Morning is an online health promotion organisation that began in 2009. Hello Sunday Morning asks participants to stop consuming alcohol for a period of time, set a goal and document their progress on a personal blog. Hello Sunday Morning is a unique health intervention for three interrelated reasons: (1) it was generated outside a clinical setting, (2) it uses new media technologies to create structured forms of participation in an iterative and open-ended way and (3) participants generate a written record of their progress along with demographic, behavioural and engagement data. This article presents a text analysis of the blog posts of Hello Sunday Morning participants using the software program Leximancer. Analysis of blogs illustrates how participants' expressions change over time. In the first month, participants tended to set goals, describe their current drinking practices in individual and cultural terms, express hopes and anxieties and report on early efforts to change. After month 1, participants continued to report on efforts to change and associated challenges and reflect on their place as individuals in a drinking culture. In addition to this, participants evaluated their efforts to change and presented their 'findings' and 'theorised' them to provide advice for others. We contextualise this text analysis with respect to Hello Sunday Morning's development of more structured forms of online participation. We offer a critical appraisal of the value of text analytics in the development of online health interventions.

Acquired Codes of Meaning in Data Visualization and Infographics: Beyond Perceptual Primitives.

While information visualization frameworks and heuristics have traditionally been reluctant to include acquired codes of meaning, designers are making use of them in a wide variety of ways. Acquired codes leverage a user's experience to understand the meaning of a visualization. They range from figurative visualizations which rely on the reader's recognition of shapes, to conventional arrangements of graphic elements which represent particular subjects. In this study, we used content analysis to codify acquired meaning in visualization. We applied the content analysis to a set of infographics and data visualizations which are exemplars of innovative and effective design. 88% of the infographics and 71% of data visualizations in the sample contain at least one use of figurative visualization. Conventions on the arrangement of graphics are also widespread in the sample. In particular, a comparison of representations of time and other quantitative data showed that conventions can be specific to a subject. These results suggest that there is a need for information visualization research to expand its scope beyond perceptual channels, to include social and culturally constructed meaning. Our paper demonstrates a viable method for identifying figurative techniques and graphic conventions and integrating them into heuristics for visualization design.

An Automated Approach to Examining Conversational Dynamics between People with Dementia and Their Carers.

The progressive neuropathology involved in dementia frequently causes a gradual decline in communication skills. Communication partners who are unaware of the specific communication problems faced by people with dementia (PWD) can inadvertently challenge their conversation partner, leading to distress and a reduced flow of information between speakers. Previous research has produced an extensive literature base recommending strategies to facilitate conversational engagement in dementia. However, empirical evidence for the beneficial effects of these strategies on conversational dynamics is sparse. This study uses a time-efficient computational discourse analysis tool called Discursis to examine the link between specific communication behaviours and content-based conversational engagement in 20 conversations between PWD living in residential aged-care facilities and care staff members. Conversations analysed here were baseline conversations recorded before staff members underwent communication training. Care staff members spontaneously exhibited a wide range of facilitative and non-facilitative communication behaviours, which were coded for analysis of conversation dynamics within these baseline conversations. A hybrid approach combining manual coding and automated Discursis metric analysis provides two sets of novel insights. Firstly, this study revealed nine communication behaviours that, if used by the care staff member in a given turn, significantly increased the appearance of subsequent content-based engagement in the conversation by PWD. Secondly, the current findings reveal alignment between human- and computer-generated labelling of communication behaviour for 8 out of the total 22 behaviours under investigation. The approach demonstrated in this study provides an empirical procedure for the detailed evaluation of content-based conversational engagement associated with specific communication behaviours.

Visualising conversation structure across time: insights into effective doctor-patient consultations.

Effective communication between healthcare professionals and patients is critical to patients' health outcomes. The doctor/patient dialogue has been extensively researched from different perspectives, with findings emphasising a range of behaviours that lead to effective communication. Much research involves self-reports, however, so that behavioural engagement cannot be disentangled from patients' ratings of effectiveness. In this study we used a highly efficient and time economic automated computer visualisation measurement technique called Discursis to analyse conversational behaviour in consultations. Discursis automatically builds an internal language model from a transcript, mines the transcript for its conceptual content, and generates an interactive visual account of the discourse. The resultant visual account of the whole consultation can be analysed for patterns of engagement between interactants. The findings from this study show that Discursis is effective at highlighting a range of consultation techniques, including communication accommodation, engagement and repetition.

Conceptual recurrence plots: revealing patterns in human discourse.

Human discourse contains a rich mixture of conceptual information. Visualization of the global and local patterns within this data stream is a complex and challenging problem. Recurrence plots are an information visualization technique that can reveal trends and features in complex time series data. The recurrence plot technique works by measuring the similarity of points in a time series to all other points in the same time series and plotting the results in two dimensions. Previous studies have applied recurrence plotting techniques to textual data; however, these approaches plot recurrence using term-based similarity rather than conceptual similarity of the text. We introduce conceptual recurrence plots, which use a model of language to measure similarity between pairs of text utterances, and the similarity of all utterances is measured and displayed. In this paper, we explore how the descriptive power of the recurrence plotting technique can be used to discover patterns of interaction across a series of conversation transcripts. The results suggest that the conceptual recurrence plotting technique is a useful tool for exploring the structure of human discourse.