2,043 research outputs found

    Using Conversation Topics for Predicting Therapy Outcomes in Schizophrenia

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    Previous research shows that aspects of doctor-patient communication in therapy can predict patient symptoms, satisfaction and future adherence to treatment (a significant problem with conditions such as schizophrenia). However, automatic prediction has so far shown success only when based on low-level lexical features, and it is unclear how well these can generalize to new data, or whether their effectiveness is due to their capturing aspects of style, structure or content. Here, we examine the use of topic as a higher-level measure of content, more likely to generalize and to have more explanatory power. Investigations show that while topics predict some important factors such as patient satisfaction and ratings of therapy quality, they lack the full predictive power of lower-level features. For some factors, unsupervised methods produce models comparable to manual annotation

    Investigating Topic Modelling for Therapy Dialogue Analysis

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    Previous research shows that aspects of doctor-patient communication in therapy can predict patient symptoms, satisfaction and future adherence to treatment (a significant problem with conditions such as schizophrenia). However, automatic prediction has so far shown success only when based on low-level lexical features, and it is unclear how well these can generalise to new data, or whether their effectiveness is due to their capturing aspects of style, structure or content. Here, we examine the use of topic as a higher-level measure of content, more likely to generalise and to have more explanatory power. Investigations show that while topics predict some important factors such as patient satisfaction and ratings of therapy quality, they lack the full predictive power of lower-level features. For some factors, unsupervised methods produce models comparable to manual annotation.

    Linguistic Indicators of Severity and Progress in Online Text-based Therapy for Depression

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    Mental illnesses such as depression andanxiety are highly prevalent, and therapyis increasingly being offered online. Thisnew setting is a departure from face-to-face therapy, and offers both a challengeand an opportunity – it is not yet knownwhat features or approaches are likely tolead to successful outcomes in such a dif-ferent medium, but online text-based ther-apy provides large amounts of data for lin-guistic analysis. We present an initial in-vestigation into the application of compu-tational linguistic techniques, such as topicand sentiment modelling, to online ther-apy for depression and anxiety. We findthat important measures such as symptomseverity can be predicted with compara-ble accuracy to face-to-face data, usinggeneral features such as discussion topicand sentiment; however, measures of pa-tient progress are captured only by finer-grained lexical features, suggesting thataspects of style or dialogue structure mayalso be important

    Helping, I Mean Assessing Psychiatric Communication: An Applicaton of Incremental Self-Repair Detection

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    18th SemDial Workshop on the Semantics and Pragmatics of Dialogue (DialWatt), 1-3 September 2014, Edinburgh, ScotlandSelf-repair is pervasive in dialogue, and models thereof have long been a focus of research, particularly for disfluency detection in speech recognition and spoken dialogue systems. However, the generality of such models across domains has received little attention. In this paper we investigate the application of an automatic incremental self-repair detection system, STIR, developed on the Switchboard corpus of telephone speech, to a new domain – psychiatric consultations. We find that word-level accuracy is reduced markedly by the differences in annotation schemes and transcription conventions between corpora, which has implications for the generalisability of all repair detection systems. However, overall rates of repair are detected accurately, promising a useful resource for clinical dialogue studies

    Non-engagement in psychosis: a narrative analysis of service-users’ experiences of relationships with mental health services

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    Introduction: Non-engagement with treatment is a familiar problem for health services and has been identified as a particularly important issue for those who experience psychosis. The therapeutic relationship between service-users and clinicians is considered to be crucial to good engagement. The extent to which requirements of engagement with treatments and mental health services represent a threat to the individual’s autonomy is a potential factor in non-engagement. Reactance theory has attempted to explain this phenomenon. However, relationships are complex and reactance theory does not reflect this. The exploration of narratives is an opportunity to develop an understanding of the intricacies of these therapeutic relationships. Methods : Interviews were conducted with 11 participants who were recovering from an episode of psychosis. Narrative Analysis of the transcripts was undertaken. During the process interpretation of the transcripts required the introduction of Dialogical Self Theory. Results: Three self-positions were identified through which participant’s narrated their experiences. Defiant, Subordinate and Reflective-Conciliatory positions were described. Discussion: Narratives surrounding recovery and engagement with services can appear complex, contradictory and fragmented. They are narrated by different self-positions. This understanding of the complexity of narratives may be helpful in guiding clinicians in maintaining a wider awareness of the multidimensional nature of individuals’ understandings of their experiences of recovery and relationships with services

    Computational Models of Miscommunication Phenomena

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    Miscommunication phenomena such as repair in dialogue are important indicators of the quality of communication. Automatic detection is therefore a key step toward tools that can characterize communication quality and thus help in applications from call center management to mental health monitoring. However, most existing computational linguistic approaches to these phenomena are unsuitable for general use in this way, and particularly for analyzing human–human dialogue: Although models of other-repair are common in human-computer dialogue systems, they tend to focus on specific phenomena (e.g., repair initiation by systems), missing the range of repair and repair initiation forms used by humans; and while self-repair models for speech recognition and understanding are advanced, they tend to focus on removal of “disfluent” material important for full understanding of the discourse contribution, and/or rely on domain-specific knowledge. We explain the requirements for more satisfactory models, including incrementality of processing and robustness to sparsity. We then describe models for self- and other-repair detection that meet these requirements (for the former, an adaptation of an existing repair model; for the latter, an adaptation of standard techniques) and investigate how they perform on datasets from a range of dialogue genres and domains, with promising results.EPSRC. Grant Number: EP/10383/1; Future and Emerging Technologies (FET). Grant Number: 611733; German Research Foundation (DFG). Grant Number: SCHL 845/5-1; Swedish Research Council (VR). Grant Numbers: 2016-0116, 2014-3

    Service Engagement and Serious Mental Illness: The Obstacles and Barriers to Attendance in a Post-Treatment Recovery Outpatient Setting

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    This qualitative study examined the factors that affect treatment adherence and service engagement in individuals with serious mental illnesses (SMI). A semistructured interview was used to collect data from treatment adherent and treatment nonadherent adults with SMI. What factors make one more or less likely to disengage from treatment? What boundaries stand in the way of quality mental-health care, and how do adults with SMI overcome these barriers? Service engagement in the population with SMI in the study was explained according to three healthcare behavioral models, the health belief model, the network episode model, and the demoralization framework model. Data collected from the narratives of 12 participants suggest that provider factors, including treatment style, theoretical orientation, and communication style, can be protective factors against systemic barriers. In light of the results of narrative data, health behavioral models that emphasize process-oriented behaviors in consideration with a broader social structure are better predictors of healthcare engagement than are rational, value-expectancy models

    The effect of clinician-patient alliance and communication on treatment adherence in mental health care: a systematic review

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    Background Nonadherence to mental health treatment incurs clinical and economic burdens. The clinician-patient alliance, negotiated through clinical interaction, presents a critical intervention point. Recent medical reviews of communication and adherence behaviour exclude studies with psychiatric samples. The following examines the impact of clinician-patient alliance and communication on adherence in mental health, identifying the specific mechanisms that mobilise patient engagement. Methods In December 2010, a systematic search was conducted in Pubmed, PsychInfo, Web of Science, Cochrane Library, Embase and Cinahl and yielded 6672 titles. A secondary hand search was performed in relevant journals, grey literature and reference. Results 23 studies met the inclusion criteria for the review. The methodological quality overall was moderate. 17 studies reported positive associations with adherence, only four of which employed intervention designs. 10 studies examined the association between clinician-patient alliance and adherence. Subjective ratings of clinical communication styles and messages were assessed in 12 studies. 1 study examined the association between objectively rated communication and adherence. Meta-analysis was not possible due to heterogeneity of methods. Findings were presented as a narrative synthesis. Conclusions Clinician-patient alliance and communication are associated with more favourable patient adherence. Further research of observer rated communication would better facilitate the application of findings in clinical practice. Establishing agreement on the tasks of treatment, utilising collaborative styles of communication and discussion of treatment specifics may be important for clinicians in promoting cooperation with regimens. These findings align with those in health communication. However, the benefits of shared decision making for adherence in mental health are less conclusive than in general medicine
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