Decision-making Process Research Articles

Pathways to experienced coercion during psychiatric admission: a network analysis

BackgroundIn mental health care, experienced coercion, also known as perceived coercion, is defined as the patient’s subjective experience of being submitted to coercion. Besides formal coercion, many other factors have been identified as potentially affecting the experience of being coerced. This study aimed to explore the interplay between these factors and to provide new insights into how they lead to experienced coercion.MethodsCross-sectional network analysis was performed on data collected from 225 patients admitted to six psychiatric hospitals. Thirteen variables were selected and included in the analyses. A Gaussian Graphical Model (GGM) using Spearman’s rank-correlation method and EBICglasso regularisation was estimated. Centrality indices of strength and expected influence were computed. To evaluate the robustness of the estimated parameters, both edge-weight accuracy and centrality stability were investigated.ResultsThe estimated network was densely connected. Formal coercion was only weakly associated with both experienced coercion at admission and during hospital stay. Experienced coercion at admission was most strongly associated with the patients’ perceived level of implication in the decision-making process. Experienced humiliation and coercion during hospital stay, the most central node in the network, was found to be most strongly related to the interpersonal separation that patients perceived from staff, the level of coercion perceived upon admission and their satisfaction with the decision taken and the level of information received.ConclusionsReducing formal coercion may not be sufficient to effectively reduce patients’ feeling of being coerced. Different factors seemed indeed to come into play and affect experienced coercion at different stages of the hospitalisation process. Interventions aimed at reducing experienced coercion and its negative effects should take these stage-specific elements into account and propose tailored strategies to address them.

FusionEXNet: an interpretable fused deep learning model for skin cancer detection

Skin cancer poses a significant burden on mankind and healthcare systems globally, necessitating the development of effective diagnostic and treatment strategies. This paper introduces FusionEXNet, an innovative and interpretable fused deep-learning model for skin cancer detection. FusionEXNet leverages the strengths of EfficientNetV2S and XceptionNet architectures to extract robust features from dermoscopic images, achieving superior performance compared to individual models. While XceptionNet and EfficientNetV2S attained accuracies of 88.82% and 88.01%, respectively, FusionEXNet surpassed them with an impressive accuracy of 90.83%. To enhance model interpretability, Explainable Artificial Intelligence (XAI) techniques, such as SmoothGrad and Faster Score-CAM, are integrated, providing valuable insights into the decision-making process. The model was trained and evaluated using the extensive HAM10000 dataset, consisting of over 10,000 high-resolution images across seven skin lesion categories. The proposed FusionEXNet model offers a reliable, accurate, and interpretable system for skin cancer detection, contributing to improved patient outcomes and more efficient diagnostic processes. This study underscores the potential of combining advanced CNN architectures with XAI methods to create powerful tools for dermatological diagnostics.

GraphPro: An interpretable graph neural network-based model for identifying promoters in multiple species

Promoters are DNA sequences that bind with RNA polymerase to initiate transcription, regulating this process through interactions with transcription factors. Accurate identification of promoters is crucial for understanding gene expression regulation mechanisms and developing therapeutic approaches for various diseases. However, experimental techniques for promoter identification are often expensive, time-consuming, and inefficient, necessitating the development of accurate and efficient computational models for this task. Enhancing the model's ability to recognize promoters across multiple species and improving its interpretability pose significant challenges. In this study, we introduce a novel interpretable model based on graph neural networks, named GraphPro, for multi-species promoter identification. Initially, we encode the sequences using k-tuple nucleotide frequency pattern, dinucleotide physicochemical properties, and dna2vec. Subsequently, we construct two feature extraction modules based on convolutional neural networks and graph neural networks. These modules aim to extract specific motifs from the promoters, learn their dependencies, and capture the underlying structural features of the promoters, providing a more comprehensive representation. Finally, a fully connected neural network predicts whether the input sequence is a promoter. We conducted extensive experiments on promoter datasets from eight species, including Human, Mouse, and Escherichia coli. The experimental results show that the average Sn, Sp, Acc and MCC values of GraphPro are 0.9123, 0.9482, 0.8840 and 0.7984, respectively. Compared with previous promoter identification methods, GraphPro not only achieves better recognition accuracy on multiple species, but also outperforms all previous methods in cross-species prediction ability. Furthermore, by visualizing GraphPro's decision process and analyzing the sequences matching the transcription factor binding motifs captured by the model, we validate its significant advantages in biological interpretability. The source code for GraphPro is available at https://github.com/liuliwei1980/GraphPro.

Integrating Deep Learning-Based Dose Distribution Prediction with Bayesian Networks for Decision Support in Radiotherapy for Upper Gastrointestinal cancer.

Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or Magnetic Resonance (MR)-guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the PTV and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

Why do students choose to do an extended GP placement?

ABSTRACT The drivers for medical students’ decision making when considering which Student Selected Component (SSC) to undertake is poorly understood. Furthermore, it is unclear why students undertake a specific SSC allowing them to have an extended placement in GP in their final year. It is known that high quality GP placements encourage students to subsequently choose GP as their career, therefore if the decision-making process of students in this area can be better understood, then this may help inform medical school actions to encourage a greater uptake of these extended placements. Semi-structured interviews were conducted with final year medical students at a Scottish University. Students were selected to provide a mixture of those who had and had not chosen to undertake the extended placement. The data was transcribed and analysed using thematic analysis to generate themes which represented the data. This showed that career intention was a major factor driving SSC choice. Additionally, students sought peer feedback and tended to avoid specific SSCs if they felt a lack of internal motivation. Considering the choice for the extended placement, students tended, again, to choose based on career intentions, and they also based their decision on previous experiences of GP. Career intention and prior experience are key factors in student choice of SSC and whether to undertake an extended GP. In order to address the national shortage of GPs medical schools need to consider how they might influence these drivers.

Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures.

The accuracy of artificial intelligence (AI) generated contours for intact-breast and post-mastectomy radiotherapy plans was evaluated. Geometric and dosimetric comparisons were performed between auto-contours (ACs) and manual-contours (MCs) produced by physicians for target structures. Breast and regional nodal structures were manually delineated on 66 breast cancer patients. ACs were retrospectively generated. The characteristics of the breast/post-mastectomy chestwall (CW) and regional nodal structures (axillary [AxN], supraclavicular [SC], internal mammary [IM]) were geometrically evaluated by Dice similarity coefficient (DSC), mean surface distance, and Hausdorff Distance. The structures were also evaluated dosimetrically by superimposing the MC clinically delivered plans onto the ACs to assess the impact of utilizing ACs with target dose (Vx%) evaluation. Positive geometric correlations between volume and DSC for intact-breast, AxN, and CW were observed. Little or anti correlations between volume and DSC for IM and SC were shown. For intact-breast plans, insignificant dosimetric differences between ACs and MCs were observed for AxNV95% (p=0.17) and SCV95% (p=0.16), while IMNV90% ACs and MCs were significantly different. The average V95% for intact-breast MCs (98.4%) and ACs (97.1%) were comparable but statistically different (p=0.02). For post-mastectomy plans, AxNV95% (p=0.35) and SCV95% (p=0.08) were consistent between ACs and MCs, while IMNV90% was significantly different. Additionally, 94.1% of AC-breasts met ΔV95% variation<5% when DSC>0.7. However, only 62.5% AC-CWs achieved the same metrics, despite AC-CWV95% (p=0.43) being statistically insignificant. The AC intact-breast structure was dosimetrically similar to MCs. The AC AxN and SC may require manual adjustments. Careful review should be performed for AC post-mastectomy CW and IMN before treatment planning. The findings of this study may guide the clinical decision-making process for the utilization of AI-driven ACs for intact-breast and post-mastectomy plans. Before clinical implementation of this auto-segmentation software, an in-depth assessment of agreement with each local facilities MCs is needed.

Rank aggregation with limited information based on link prediction

Rank aggregation is a vital tool in facilitating decision-making processes that consider multiple criteria or attributes. While in many applications, the available ranked lists are often limited and quite partial for various reasons. This scarcity of ranking information presents a significant challenge to rank aggregation effectiveness. To address this problem of rank aggregation with limited information, in this study, on the basis of networked representation of ranking information, we employ the link prediction technology to mine potential ranking information. It aims to optimize the aggregation process, and maximize the aggregation effectiveness using available limited information. Experimental results indicate that our proposed approach can significantly enhance the aggregation effectiveness of existing rank aggregation methods, such as Borda’s method, competition graph method and Markov chain method. Our work provides a new way to solve rank aggregation problem with limited information and develops a new research paradigm for future rank aggregation studies from the perspective of network science.

An analysis of public topics and sentiments based on social media during the COVID-19 Omicron Variant outbreak in Shanghai 2022

The outbreak of the COVID-19 Omicron variant in Shanghai in 2022 elicited complex emotions among Shanghainese during the two-month quarantine period. This paper aims to identify prevailing public themes and sentiments by analyzing social media posts from Weibo. Initially, we conducted research based on a dataset of 90,000 Weibo posts during the 2022 COVID-19 outbreak in Shanghai. By examining social media data that mirrors residents' emotional shifts and areas of focus during unforeseen circumstances, we have developed an analytical framework combining hotspot analysis and public sentiment assessment. Subsequently, we employed the Latent Dirichlet Allocation (LDA) method to conduct topic modeling on the Weibo text data. The SnowNLP sentiment classification method was then utilized to quantify sentiment values. Ultimately, we performed spatial visualization of sentiment and concern data, categorizing them into distinct time periods based on Shanghai's infection curve. This approach allowed us to investigate concern focal points, sentiment trends, and their spatiotemporal evolution characteristics. Our findings indicate that variations in public sentiment primarily hinge on the severity of the epidemic's spread, emerging events, the availability of essential resources, and the government's ability to respond promptly and accurately. It is evident that, while residents' concerns shift over time, their primary objective on social media remains expressing demands and releasing emotions. This research offers an avenue for leveraging public opinion analysis to enhance governance capacity during crises, fortify urban resilience, and promote public involvement in governmental decision-making processes.

“I didn’t even wonder why I was on the floor” – mixed methods exploration of stroke awareness and help-seeking behaviour at stroke symptom onset

IntroductionTo better target stroke awareness efforts (pre and post first stroke) and thereby decrease the time window for help-seeking, this study aims to assess quantitatively whether stroke awareness is associated with appropriate help-seeking at symptom onset, and to investigate qualitatively why this may (not) be the case.MethodsThis study conducted in a German regional stroke network comprises a convergent quantitative-dominant, hypothesis-driven mixed methods design including 462 quantitative patient questionnaires combined with qualitative interviews with 28 patients and seven relatives. Quantitative associations were identified using Pearson’s correlation analysis. Open coding was performed on interview transcripts before the quantitative results were used to further focus qualitative analysis. Joint display analysis was conducted to mix data strands. Cooperation with the Patient Council of the Department of Neurology ensured patient involvement in the study.ResultsOur hypothesis that stroke awareness would be associated with appropriate help-seeking behaviour at stroke symptom onset was partially supported by the quantitative data, i.e. showing associations between some dimensions of stroke awareness and appropriate help-seeking, but not others. For example, knowing stroke symptoms is correlated with recognising one’s own symptoms as stroke (r = 0.101; p = 0.030*; N = 459) but not with no hesitation before calling help (r = 0.003; p = 0.941; N = 457). A previous stroke also makes it more likely to recognise one’s own symptoms as stroke (r = 0.114; p = 0.015*; N = 459), but not to be transported by emergency ambulance (r = 0.08; p = 0.872; N = 462) or to arrive at the hospital on time (r = 0.02; p = 0.677; N = 459). Qualitative results showed concordance, discordance or provided potential explanations for quantitative findings. For example, qualitative data showed processes of denial on the part of patients and the important role of relatives in initiating appropriate help-seeking behaviour on patients’ behalf.ConclusionsOur study provides insights into the complexities of the decision-making process at stroke symptom onset. As our findings suggest processes of denial and inabilities to translate abstract disease knowledge into correct actions, we recommend to address relatives as potential saviours of loved ones, increased use of specific situational examples (e.g. lying on the bathroom floor) and the involvement of patient representatives in the preparation of informational resources and campaigns. Future research should include mixed methods research from one sample and more attention to potential reporting inconsistencies.

Climate storylines as a tool for interdisciplinary dialogue on risk decision-making: Analyzing a severe drought in southeastern South America

Recently, we are witnessing a growing interest in the study of climate narratives and storylines. These are seen as critical tools both for improving shared understandings involving different stakeholders (including scientists from different disciplines) and guiding initiatives for transformative adaptation actions and decision making. This paper approaches the storylines about a severe drought event that took place in southeastern South America (SESA) in 2008/2009 from an interdisciplinary dialogue between climatologists and anthropologists. This approach responds to the need for a more robust understanding of the event and the responses adopted locally, i.e: not restricted to physical aspects but considering socio-economic and political aspects, with the aim of contributing to the development of socially relevant climatic information for realistic climate risk decision making contexts. In particular it intends to contribute to the decision-making processes of government agencies focused on climate communication and public climate services, such as national and weather services and regional agencies. Considering disciplinary differences, we account for the interpretative polyphony regarding this event, i.e.: what each actor/sector is talking about (for what and to whom they are talking about) when referring to "the drought", and analyze climate storylines as an agonistic field of meanings. This implies recognizing the disputes and contradictions among them, how different storylines promote certain lines of argumentation and meanings to the detriment of others, knowledge gaps, the role of social power relations, socio-political order factors and their impact in terms of the necessary transformations towards an environmentally sustainable and socially just horizon.

Application of near-infrared spectroscopy as at line method for the evaluation of histamine in tuna fish (Thunnus albacares)

The assessment of histamine levels in fishery products has emerged as a paramount issue in the context of global food safety, given its profound implications for human health and the consequential impact on food quality and trade. Histamine intoxication, stemming from the ingestion of foods containing heightened histamine levels, results from the bacterial decarboxylation of histidine under conditions of improper handling, processing, or storage. This study endeavors to provide a thorough examination of histamine contamination in frozen-thawed tuna (Thunnus albacares) samples, employing an integrated approach that combines near-infrared spectroscopy (NIRS) with advanced machine learning techniques. One hundred and one samples were considered, and a systematic fortification process was applied to obtain samples with 4 histamine concentrations (0; 50; 150; 250 mg/kg); the fortification levels were confirmed by the LC-MS/MS analysis. Subsequently, NIRS spectra were collected and chemometric analyses, including modified partial least squares regression (MPLS) and support vector machine (SVM), were employed for quantitative and qualitative evaluation, respectively. Histamine quantification through MPLS utilizing the full spectrum exhibited good predictive performance in cross-validation and in hold-out validation (R2CV = 0.88; R2P = 0.74, respectively), confirming the potential of NIRS for estimating histamine levels in tuna. SVM classification models, both binary (presence/absence) and multiclass (four levels), demonstrated high accuracy (100% and 93%, respectively). The study highlights the effectiveness of NIRS combined with machine learning for rapid and accurate histamine detection in frozen-thawed tuna, offering a non-destructive, environmentally friendly alternative to traditional methods. This approach holds significant promise for food business operators and regulatory authorities, enhancing product safety, quality control, and decision-making processes related to histamine contamination in the seafood industry.

Impact assessment of multiple pressures on ecosystem services with a state and transition model: Application to Posidonia oceanica seagrass meadows

A significant challenge in the integration of ecosystem services into decision-making processes lies in effectively capturing the dynamics of marine socio-ecological systems, including their evolutionary pathways, equilibrium states, and tipping points. This paper explores the evolutionary trajectories of a vital marine ecosystem endemic to the Mediterranean Sea: the Posidonia oceanica seagrass meadows, in response to various drivers of change. A state-and-transition model is employed to assess the ecosystem services provided by P. oceanica across different states defined by selected transitions, such as overfishing, fragmentation, pollution, and invasion by non-native species. To apply this model, scientific expertise is combined with field data generated using the Ecosystem-Based Quality Index to evaluate the conservation status of P. oceanica. This integrated approach allows for the representation of the ecosystem services offered by the meadows across different states, leveraging ecological data. The findings highlight the disproportionate impact on provisioning services, particularly sea urchins and commercial fish production, which suffer the most under various stressors. Notably, when these services decline to critical levels, the meadows cease to provide significant benefits. Finally, a synthesized representation is presented, merging ecological insights with monitoring data, offering a framework that is more accessible to stakeholders and decision-makers.

Business Judgment Rule and Corporate Governance as the Strategic Imperative of Indonesian State-owned Enterprise

The business judgment rule in state-owned enterprises can serve as either a protective shield or a boomerang for those involved within the mechanism of corporate governance. As such, this study aims to explore the relationship between good corporate governance and the business judgment rule principle in the context of the integrity and sustainability of directorial decisions amidst the dynamically changing context of a company’s legal responsibilities. Through a juridical normative method employing a conceptual approach, this study finds that good corporate governance cannot determine the implementation of the business judgment rule principle. This means that even though good corporate governance is significant in creating a conducive work environment for a responsible decision-making process, it cannot directly influence the implementation of the business judgment rule in cases involving state-owned enterprises.

Implementasi Kombinasi LOPCOW dan Operational Competitiveness Rating Analysis Dalam Rekomendasi Tempat Wisata Indoor

Indoor attractions offer an exciting and comfortable holiday experience, especially for those who want to avoid extreme weather or want indoor comfort. The selection of indoor attractions is often faced with various problems involving distance, price, rating, and cleanliness. All these factors must be carefully considered to ensure that the chosen indoor attraction can provide a satisfying and enjoyable experience. The application of a combination of Logarithmic Percentage Change-Driven Objective Weighting (LOPCOW) and Operational Competitiveness Rating Analysis (OCRA) in the performance evaluation of business alternatives offers an innovative and robust approach to decision making. LOPCOW allows objective determination of criteria weighting based on logarithmic percentage changes, emphasizing the dynamics of relative performance changes between criteria. Integration with OCRA, which evaluates operational competitiveness through efficient ratio analysis. The ranking results showed that the first best rank was obtained by Puncak Mas with a final value of 1.2596, the second-best rank was obtained by Wira Garden with a final value of 1.1607, the third best rank was obtained by Lampung Walk with a final value of 1.0289. The combination of these two methods increases the accuracy and reliability of ranking results, assisting decision makers in choosing truly superior alternatives based on relevant and up-to-date data. By utilizing data-driven analysis and robust methodologies, the decision-making process becomes more efficient and can be done faster, saving time and resources.

Orientation and mental representations in the decision-making process: a comparison between junior and senior professional

In the workplace, professionals have an orientation to individual or group decision-making based on ideal or real representation of the group situation. However, no previous studies investigated these dimensions in professionals of different ages. A total of 216 professionals were enrolled in this study based on their work experience and seniority. Specifically, senior professionals were from different Italian consulting companies, while juniors had not yet approached the world of work, but were completing their professional education. Participants were asked to read a realistic decision-making scenario and rate their individual or group decision-making orientation, thinking of an ideal and real group situation. A stronger group decision-making orientation was found in both groups of professionals, independent of age. However, professionals appear less group-oriented when they mentally self-represent their real compared to their ideal group. Additionally, in senior professionals, a positive correlation between individual and group orientation was found in the ideal group situation. Indeed, in junior professionals, a positive correlation between individual and group orientation was found in the real situation. Therefore, it might be that becoming older and increasing professionalism level correlate with awareness of the difference between real and ideal group representations.

Conflict Avoidance &amp; Resolution: New Approaches through Conversations and Decision-Making Processes

Conflict Avoidance &amp; Resolution: New Approaches through Conversations and Decision-Making Processes

Prevalence of Counterfeit Fashion Goods in a West African Country: Crime in Perspective

ABSTRACT This study explores the prevalence of counterfeit fashion goods in a West African country within the context of crime, focusing on the perspectives of producers, distributors, and consumers. Drawing on rational choice theory, the research aims to understand the awareness, motivations and decision-making processes behind the production, distribution, and consumption of counterfeit fashion items in Ghana. A qualitative research approach was adopted, utilizing snowball sampling to gather data from eight producers, eight distributors, and 25 consumers of counterfeit fashion goods. Semi-structured interviews were conducted to explore participants’ awareness, perceptions, rational decisions and behaviors regarding counterfeit fashion goods. The findings underscore the need for stronger regulatory frameworks and public education initiatives to combat the proliferation of counterfeit fashion goods in Ghana, West Africa. Recommendations include implementing stricter laws and enforcement mechanisms, enhancing consumer awareness about the risks associated with counterfeit products, and fostering collaboration among stakeholders to address this complex issue effectively.

Capturing the complexity of child behavior and caregiver-child interactions in the HEALthy Brain and Child Development (HBCD) Study using a rigorous and equitable approach

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. This article outlines methodological considerations and the decision-making process for measurement selection for child behavior, parenting/caregiver-child interactions, and the family/home environment for HBCD. The decision-making process is detailed, including formation of a national workgroup (WG-BEH) that focused on developmentally appropriate measures that take a rigorous and equitable approach and aligned with HBCD objectives. Multi-level-observational and caregiver-report measures were deemed necessary for capturing the desired constructs across multiple contexts while balancing the nuance of observational data with pragmatic considerations. WG-BEH prioritized developmentally sensitive, validated assessments with psychometrics supporting use in diverse populations and focused on mechanistic linkages and prediction of desired constructs. Other considerations included participant burden and retention, staff training needs, and cultural sensitivity. Innovation was permitted when it was grounded in evidence and filled key gaps. Finally, this article describes the rationale for the selected constructs (e.g., temperament, social-emotional development, parenting behaviors, family organization) and corresponding measures chosen for HBCD visits from early infancy through 17 months of age.

Multi-modal incomplete label information three-way bidirectional decision-making: Applications of disease assessment

Disease assessment involves two stages of decision-making process. The first process is to infer the development trend of the disease through the existing judgment conditions, which is to make a positive estimation (prediction) decision. The second process is to update the judgment conditions for different disease states and optimize the clinical assessment evidence by fusing disease progression data, that is, to make a reverse evaluation (optimization) decision by reviewing historical diagnostic and treatment data to summarize the best judgment conditions for the disease characteristics. Therefore, we construct a three-way bidirectional decision method for multi-modal incomplete labeling information, considering the mixed data features with no labels and labels, and the bidirectional decision features of positive prediction and reverse optimization. Specifically, the method first introduces semi-supervised learning (SSL) into the rough sets, constructs a rough sets model based on cosine similarity relation, generates pseudo-labels for incomplete information, and explains the positive prediction decision process principle. Second, the gradient space is constructed on the predictive information system by the three-way decision-making principle, the loss function formed by the combination of KL divergence and cross entropy loss function is used to find the direction of the fastest decline in attribute information value, and the optimal decision rule and the automatic updating strategy of the threshold value are obtained. At the same time, the principle of the reverse optimization decision process is explained. Finally, multimodal information from pathological tissue images is used to evaluate clinical progress problem. A pathological tissue images dataset includes 2000 colorectal cancer and 300 kidney disease cases used for simulation experiments. The proposed three-way bidirectional decision-making model can be widely applied to actual decision-making scenarios for evaluating clinical progress of different diseases. Compared with previous studies, this method can realize objective calculation and automatic update of threshold and loss values and effectively solve complex prediction and optimization decision-making problems.

Indication and Contraindication of Video-Based Psychotherapy: A Qualitative Delphi Survey to Capture Psychotherapists' Reflection and Deliberation Processes

The Corona pandemic forced many psychotherapists to make a rapid transition to video setting. Even after the acute phase of the pandemic, video-based treatments can be determined to a certain extent in practice. Thus, indication and contraindication recommendations are essential to ensure treatment quality. However, empirical studies are almost completely lacking in this regard. In the context of this work, the decision-making and consideration processes of psychotherapists regarding video-based treatments are to be recorded and those factors that can have an effect on the indication are to be identified. Two-stage, qualitative Delphi survey with psychodynamically oriented psychotherapists (n=9) and behavioural therapists (n=7) who can be assigned to the fields of research and practice. The quality of the therapeutic alliance guides the participants' choice of setting. An indication for video setting results from the feasibility of therapeutic interventions and the respective treatment goal. The therapists weighed the costs and benefits of video setting for various disorders. The changed feeling of control on both sides and the possibility of improving health care are decisive factors. Based on the results, a decision tree for psychotherapists was created, which includes the identified indication criteria.