ArticlePloS one2024
Improving patient satisfaction based on service quality in clinical trials: A cross-sectional study.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It carries an expression of concern. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Interpretable SHAP-based machine learning framework for patient satisfaction prediction: a case study in Thammasat University Hospital.BMC medical informatics and decision making · 2026Article
- Exploring the Perspectives of Patients and Healthcare Providers on Rheumatology Clinical Trials: A Single-Center Cross-Sectional Study in Hungary.Journal of clinical medicine · 2026Article
- Expression of Concern: Improving patient satisfaction based on service quality in clinical trials: A cross-sectional study.PloS one · 2026Article
- Assessment of Participant Satisfaction and Overall Experience: A Cross-Sectional Survey to Inform Trial Conduct.Patient preference and adherence · 2026Article
- A Cross-Sectional Latent Class Analysis of 3 Distinct Satisfaction Subgroups Among Non-Local Patients in VIP Outpatient.Inquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
- Expression of concern
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundParticipants' satisfaction is an important factor in securing competitiveness in clinical trials. In many industries, such as healthcare, customer service quality has been analyzed to increase customer satisfaction. However, no study so far has attempted to measure participants' perceptions of service quality in the clinical trial area and identify its effect on participant satisfaction.
objectiveThis study examined the experiences and perceptions of clinical trial participants in terms of service quality and identified the factors that impact participant satisfaction in clinical trials.
methodsThis study used a cross-sectional descriptive and explanatory research design. Data were collected from March 29 to May 26, 2023, via a survey. The survey was conducted with 206 adults participating in clinical trials at two hospitals in Korea. The collected data were analyzed using descriptive statistics, independent t-tests, one-way ANOVA, Pearson's correlation, and multiple linear regression analysis.
resultsParticipants' perceptions of the service quality and their satisfaction in clinical trials were generally positive. The variables that significantly predicted participant satisfaction in clinical trials included quality of interaction with researchers, physical environment, performance results in clinical trials, changes in health status after participating in the trial, and consideration of discontinuing the trial.
conclusionsParticipants' perception of the service quality significantly affected their satisfaction in clinical trials. Thus, all components of service quality should be considered in the overall clinical trial process to increase participants' satisfaction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.