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400 results for “Trust”
Impact on Mortality of Screening for Kidney Disease Associated With a Specialized Intervention During Hospitalization in a Territorial Hospital Trust
ClinicalTrials.gov study NCT05033652. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Building TrUst and UNiting Teams Through DouLa partnErship - BUNDLE
ClinicalTrials.gov study NCT05993689. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Pilot Feasibility Study of IM OXT on Fear of Pain and Trust in Physical Therapist During OP Rehabilitation After TKA
ClinicalTrials.gov study NCT04493229. IPD Sharing: NO. Countries: 1. Publications: 0.
"TransRadial Ultra Support Technique" (TRUST)
ClinicalTrials.gov study NCT03025685. IPD Sharing: NO. Countries: 1. Publications: 0.
TReating Unhelpful Suspicious Thoughts in Adolescents (TRUST): a School-based Case Series
ClinicalTrials.gov study NCT06975228. IPD Sharing: Not stated. Countries: 1. Publications: 0.
WE-TRUST (Workflow Optimization to Reduce Time to Endovascular Reperfusion for Ultra-fast Stroke Treatment)
ClinicalTrials.gov study NCT04701684. IPD Sharing: NO. Countries: 9. Publications: 0.
Randomizing Vignettes to Understand Contributors to Trust in Primary Care Doctors
ClinicalTrials.gov study NCT06526663. IPD Sharing: YES. Countries: 1. Publications: 0.
Effects of "Parenting in Sweden" on Trust in Social Services
ClinicalTrials.gov study NCT06874998. IPD Sharing: NO. Countries: 1. Publications: 0.
Placing Trust in Endoscopic Ultrasonography: Impact on Planning Conformal Radiotherapy of Cancer of the Esophagus and Rectum
ClinicalTrials.gov study NCT02526134. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Testing a Device for Automatically TRacking Urine and STool in an Inpatient HCT Setting (Project TRUST)
ClinicalTrials.gov study NCT05520346. IPD Sharing: NO. Countries: 1. Publications: 0.
TRUST-2: Safety and Efficacy of Intravenous Remodulin® in Patients in India With Pulmonary Arterial Hypertension (PAH)
ClinicalTrials.gov study NCT03055221. IPD Sharing: NO. Countries: 0. Publications: 0.
Extraversion and Trust Conformity
<p>Data for Extraversion-Trust Conformity Study.</p>
In art we trust
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1653128989.1098.jpg">https://4dcity.org/imgupload/1653128989.1098.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/52014953307_db940d4904_m.jpg">https://live.staticflickr.com/65535/52014953307_db940d4904_m.jpg</a> <br><br><u>Image-Metadata:</u><br>Filename: 1653128989.1098.jpg<br>Image Dimensions: 218x240<br>Megapixels: 0.05 MP<br>Filesize: 44.82 KB<br><br>ExifOffset: 26
Tenecteplase in ReperfUsion Therapy for Posterior Circulation Stroke With Extended Time Window: the TRUST Trial
ClinicalTrials.gov study NCT07073469. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Nurse-led Reminder Program for Therapy Adherence, Clinical Outcomes and Trust in Nurses in Diabetic Patients
ClinicalTrials.gov study NCT02196272. IPD Sharing: Not stated. Countries: 0. Publications: 0.
TrustMus benchmark: The Role of Large Language Models in Musicology: Are We Ready to Trust the Machines?
<p>TrustMus is an initial, rigorously validated benchmark designed to assess the accuracy and reliability of large language models (LLMs) in the domain of musicology. This dataset includes a collection of 400 human-validated multiple-choice questions, categorized into four thematic areas: People (Ppl), Instruments and Technology (I&T), Genres, Forms, and Theory (Thr), and Culture and History (C&H).</p> <p>The questions are derived from <em>The Grove Dictionary Online</em> using a semi-automated methodology. The process involves generating initial questions with a fine-tuned retrieval-augmented generation (RAG) model, filtering them through a series of automated checks, and finally validating them through expert human annotation. TrustMus is introduced in an initial paper, providing a critical resource for researchers and developers aiming to evaluate and improve LLM performance in this specialized field of musicology.</p> <p>This benchmark is discussed in the paper : </p> <p><strong>BibTeX Citation:</strong></p> <div> <div><code>@inproceedings{ramoneda2024trustmus,</code><br><code> title={The Role of Large Language Models in Musicology: Are We Ready to Trust the Machines?},</code><br><code> author={Ramoneda, Pedro and Parada-Cabaleiro, Emilia and Weck, Benno and Serra, Xavier},</code><br><code> booktitle={Proceedings of the 3rd Workshop on NLP for Music and Audio (NLP4MusA)},</code><br><code> year={2024},</code><br><code> month={November},</code><br><code> address={San Francisco, USA},</code><br><code> organization={Co-located with ISMIR'2024}</code><br><code>}</code><br><br> <div> </div> </div> </div>
MuSe-Trust: Multimodal Trustworthiness Sub-challenge (MuSe2020)
<p><strong>MuSe-Trust of MuSe2020:</strong> Predicting the level of trustworthiness of user-generated audio-visual content in a sequential manner utilising a diverse range of features and (optional) emotional (arousal and valence) predictions. This package includes only MuSe-Trust features (all partitions) and annotations of the training and development set (test scoring via the MuSe website). </p> <p><strong>General:</strong> The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). </p> <p>We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (>36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.</p> <p>We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of closeup, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.</p>
The Role of AI Chatbots on E-Commerce Platforms: Understanding Its Influence on Customer Trust and Dependability
Open the record for dataset details and reuse information.
Influencing A Nation: How a Leader's Interpersonal Emotion Regulation Influences Citizen Compliance via Trust and Emotions during a Global Pandemic
<p>During crises like the COVID-19 pandemic, it was necessary for political leaders to influence citizens to comply with public health measures and restrictions. These health measures (e.g., physical distancing, staying at home) had substantial negative effects on individuals lives and thus were sometimes met with defensive, non-cooperative responses. To influence citizens’ compliance with public health guidance and nationally imposed restrictions, political leaders needed to effectively motivate them through their public communications. We argue that whilst negative emotions may have discouraged citizens from deviating from public health restrictions, other factors such as citizens’ trust in political leaders played a role as well. We investigated whether the perception of the interpersonal emotion regulation (IER) strategies used by government leaders in ministerial briefings impacted citizens’ compliance intentions via either negative affect or perceived trustworthiness. Across three studies based in Western Europe (Studies 1 & 2 survey, Study 3 experimental), we consistently found that a leader’s affect-improving IER strategies increased compliance intentions via perceived trustworthiness but not via negative affect. Affect-worsening IER strategies demonstrated either no effect or an indirect worsening effect on the compliance intentions of citizens. Our findings highlight the importance of IER strategies in ministerial briefings and perceived trustworthiness of political leaders in motivating citizens to comply with public health restrictions during a pandemic. </p>
Conspiracy, Government trust, and Vaccination
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OpenNeuro
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