scholarly journals Routine Outcome Monitoring in Psychotherapy Treatment Using Sentiment-Topic Modelling Approach

2021 ◽  
Vol 11 (6) ◽  
pp. 2428
Author(s):  
Noor Fazilla Abd Yusof ◽  
Chenghua Lin
Keyword(s):  
Routine Outcome Monitoring ◽  
Topic Modelling ◽  
Outcome Monitoring ◽  
Modelling Approach

Clinical dilemmas of routine outcome monitoring and clinical feedback: a qualitative study of patient experiences

2020 ◽  
Author(s):  
Stig Magne Solstad ◽  
Gøril Kleiven Solberg ◽  
Louis George Castonguay ◽  
Christian Moltu
Keyword(s):  
Mental Health ◽  
Clinical Skill ◽  
Routine Outcome Monitoring ◽  
Future Research ◽  
Successful Implementation ◽  
Basic Training ◽  
Feedback Systems ◽  
Clinical Encounters ◽  
Outcome Monitoring ◽  
Clinical Feedback

Purpose: Routine outcome monitoring (ROM) and clinical feedback systems (CFS) are becoming prevalent in mental health services. The field faces several challenges to successful implementation. The purpose of this study is to gain a better understanding of these challenges by exploring the patient perspective. Method: We report the findings from a qualitative, video assisted interview study of 12 patients from a Norwegian mental health outpatient clinic using ROM/CFS. Results: Our analysis resulted in three pairs of opposing experiences with using ROM/CFS: 1) Explicit vs. implicit use of CFS information, 2) CFS directing focus towards- vs. directing focus away from therapeutic topics and 3) Giving vs. receiving feedback. None of these were intrinsically helpful or hindering. Participants had vastly differing preferences for how to use ROM/CFS in clinical encounters, but all needed the information to be used in a meaningful way by their therapists. If not, ROM/CFS was at risk of becoming meaningless and hindering for therapy. Conclusion: These findings confirm and provide further nuance to previous research. We propose to consider ROM/CFS a clinical skill that should be a part of basic training for therapists. How to use and implement ROM/CFS skillfully should also be the focus of future research.

Analysing political events on Twitter

2019 ◽  
Vol 53 (1) ◽  
pp. 38-39
Author(s):  
Anjie Fang
Keyword(s):  
Topic Model ◽  
Ground Truth ◽  
Topic Modelling ◽  
Time Dimension ◽  
Social Scientists ◽  
Political Events ◽  
Community Classification ◽  
Ground Truth Generation ◽  
Twitter Users ◽  
Modelling Approach

Recently, political events, such as elections, have raised a lot of discussions on social media networks, in particular, Twitter. This brings new opportunities for social scientists to address social science tasks, such as understanding what communities said or identifying whether a community has an influence on another. However, identifying these communities and extracting what they said from social media data are challenging and non-trivial tasks. We aim to make progress towards understanding 'who' (i.e. communities) said 'what' (i.e. discussed topics) and 'when' (i.e. time) during political events on Twitter. While identifying the 'who' can benefit from Twitter user community classification approaches, 'what' they said and 'when' can be effectively addressed on Twitter by extracting their discussed topics using topic modelling approaches that also account for the importance of time on Twitter. To evaluate the quality of these topics, it is necessary to investigate how coherent these topics are to humans. Accordingly, we propose a series of approaches in this thesis. First, we investigate how to effectively evaluate the coherence of the topics generated using a topic modelling approach. The topic coherence metric evaluates the topical coherence by examining the semantic similarity among words in a topic. We argue that the semantic similarity of words in tweets can be effectively captured by using word embeddings trained using a Twitter background dataset. Through a user study, we demonstrate that our proposed word embedding-based topic coherence metric can assess the coherence of topics like humans [1, 2]. In addition, inspired by the precision at k metric, we propose to evaluate the coherence of a topic model (containing many topics) by averaging the top-ranked topics within the topic model [3]. Our proposed metrics can not only evaluate the coherence of topics and topic models, but also can help users to choose the most coherent topics. Second, we aim to extract topics with a high coherence from Twitter data. Such topics can be easily interpreted by humans and they can assist to examine 'what' has been discussed and 'when'. Indeed, we argue that topics can be discussed in different time periods (see [4]) and therefore can be effectively identified and distinguished by considering their time periods. Hence, we propose an effective time-sensitive topic modelling approach by integrating the time dimension of tweets (i.e. 'when') [5]. We show that the time dimension helps to generate topics with a high coherence. Hence, we argue that 'what' has been discussed and 'when' can be effectively addressed by our proposed time-sensitive topic modelling approach. Next, to identify 'who' participated in the topic discussions, we propose approaches to identify the community affiliations of Twitter users, including automatic ground-truth generation approaches and a user community classification approach. We show that the mentioned hashtags and entities in the users' tweets can indicate which community a Twitter user belongs to. Hence, we argue that they can be used to generate the ground-truth data for classifying users into communities. On the other hand, we argue that different communities favour different topic discussions and their community affiliations can be identified by leveraging the discussed topics. Accordingly, we propose a Topic-Based Naive Bayes (TBNB) classification approach to classify Twitter users based on their words and discussed topics [6]. We demonstrate that our TBNB classifier together with the ground-truth generation approaches can effectively identify the community affiliations of Twitter users. Finally, to show the generalisation of our approaches, we apply our approaches to analyse 3.6 million tweets related to US Election 2016 on Twitter [7]. We show that our TBNB approach can effectively identify the 'who', i.e. classify Twitter users into communities. To investigate 'what' these communities have discussed, we apply our time-sensitive topic modelling approach to extract coherent topics. We finally analyse the community-related topics evaluated and selected using our proposed topic coherence metrics. Overall, we contribute to provide effective approaches to assist social scientists towards analysing political events on Twitter. These approaches include topic coherence metrics, a time-sensitive topic modelling approach and approaches for classifying the community affiliations of Twitter users. Together they make progress to study and understand the connections and dynamics among communities on Twitter. Supervisors : Iadh Ounis, Craig Macdonald, Philip Habel The thesis is available at http://theses.gla.ac.uk/41135/

scholarly journals Treatment course and its predictors in patients with somatoform disorders: A routine outcome monitoring study in secondary psychiatric care

2018 ◽  
Vol 25 (4) ◽  
pp. 550-564
Author(s):  
I.V.E. Carlier ◽  
D.H. Andree Wiltens ◽  
Y.R. van Rood ◽  
T. van Veen ◽  
J. Dekker ◽  
...  
Keyword(s):  
Psychiatric Care ◽  
Somatoform Disorders ◽  
Routine Outcome Monitoring ◽  
Outcome Monitoring ◽  
Monitoring Study

Evaluation of an Online System for Routine Outcome Monitoring: A Cross-Sectional Survey (Preprint)

2021 ◽  
Author(s):  
Deanna Wiebe ◽  
Pria Nippak ◽  
Julien Meyer ◽  
Shannon Remers
Keyword(s):  
Mental Health ◽  
Outcome Measures ◽  
Service Providers ◽  
Routine Outcome Monitoring ◽  
Completion Rates ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Online System ◽  
Health Service Providers ◽  
Outcome Monitoring

BACKGROUND The use of Routine Outcome Monitoring (ROM) in the treatment of mental health has emerged as a method of improving psychotherapy treatment outcomes. Despite this, very few clinicians regularly use ROM in clinical practice. Online ROM has been suggested as a solution to increase adoption. OBJECTIVE To identify the influence of moving ROM online on client completion rates of self-reported outcome measures and to identify implementation and utilization barriers to online ROM by assessing clinicians’ views on their experience utilizing the online system over previous paper-based methods. METHODS Client completion rates of self-reported outcome measures were compared pre and post implementation of an online system of ROM. In addition, a survey questionnaire was administered to 340 mental health service providers regarding their perception of benefits with an online system of ROM. RESULTS Client completion rates of self-reported measures increased from 15% to 54% after moving online. Fifty-eight% of service providers found the new system less time consuming than previous paper-based ROM and 64% found that it helped monitor clients. However, the perceived value of the system remains in doubt as only 23% found it helped them identify clients at risk for treatment failure, and only 18% found it strengthened the therapeutic alliance. CONCLUSIONS Although the current study suggests mixed results regarding service providers’ views on their experience using an online system for ROM, it has identified barriers and challenges that are actionable for improvement.

scholarly journals Vers un modèle explicatif de l’effet du Routine Outcome Monitoring : De la communication non verbale au feedback instrumental.

2018 ◽  
Author(s):  
Christophe Cazauvieilh
Keyword(s):  
Routine Outcome Monitoring ◽  
Outcome Monitoring

L’utilisation des systèmes de Suivi Continu des Résultats (SCR ou Routine Outcome Monitoring - ROM) représente une méthode prometteuse pour aider les cliniciens à évaluer et à détecter les patients à risque de ne pas retirer de bénéfices réels des traitements psychothérapeutiques ou de l’abandonner prématurément ; ainsi que pour améliorer l’efficacité de soins avec cette population. Les effets dus à l’utilisation du feedback sont pourtant mal compris, et cette étude vise à proposer un modèle générique des effets du feedback en psychothérapie.

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