A scale for measuring healthcare service quality incorporating patient-centred care and using a psychometric analytics framework

Abstract

PurposeRecent developments in healthcare contexts increasingly emphasize patient-centred approaches to service quality measures; however, few studies consider this dimension explicitly. The present study develops and psychometrically validates a scale of healthcare service quality explicitly incorporating a patient-centred care dimension from a communicational perspective. The paper also enriches the traditional content of service quality by including equity items and presents the underlying structure of service quality in an emerging country.Design/methodology/approachThe final sample consisted of 869 healthcare users (complete cases in the service quality items derived from 917 surveys received) from Colombia. The authors used a psychometric analytics framework comprising seven processes incorporating exploratory factor analysis, structural equation modelling, and machine learning methods to examine construct plausibility, reliability, construct validity, equity, and criterion/predictive validity (e.g. explaining/predicting subjective well-being and behavioural intentions).FindingsThe final scale consists of 17 items and satisfies all psychometric properties. Its validation allows for the discovery and psychometrical confirmation of two essential dimensions: patient-centred communication (eight items) and process quality (nine items).Practical implicationsThe authors illustrate three practical uses of the scale: the possibility for diagnoses; hypothesis contrast based on confidence intervals; and estimation of the capacity of the service to satisfy specifications.Originality/valueBoth dimensions reveal users' relevant needs and complement previous studies that have focused on process aspects of healthcare service quality.

Application of Sports Equipment Image Intelligent Recognition Response APP in Sports Training and Teaching

“…Literature [11] used Keep exercise software to intervene in students' exercise, analyzed the students' strength quality, speed quality and endurance quality before and after the intervention application, and used the Extreme Learning Machine network to construct the application analysis model, which improved the students' mastery of APP use technology and developed good exercise habits; Literature [12] used the third-party application of fitness guidance APP for smartphones or wearable devices. It analyzed the situation of users recording fitness data, guiding sports learning, configuring sports resources, and leading a healthy lifestyle; Literature [13] analyzed the feeling of using the intelligent recognition response APP for sports equipment images from the three aspects of the APP function experience, interaction, and content experience, combined with the shallow neural network technology; Literature [14] optimized the parameters of the support vector machine using the particle swarm optimization algorithm to build a intelligent sports APP use analysis model, input APP use analysis model from five dimensions of expectation confirmation, satisfaction, perceived ease of use, perceived usefulness and trust; Literature [15] analyzes the driving factors of sports equipment fitness APP use, and constructs the APP application analysis system and model considering five dimensions of habit, game sense, health consciousness, performance sense value and price value; Literature [16] analyzes the use of fitness sports APP in extracurricular physical exercise monitoring and proposes the use evaluation effect analysis method based on intelligent algorithm optimization depth network. Although the design and application of sports APP has been widely used, the expansion of the functions of sports APP software and the analysis of the application effect still needs to be solved and developed and researched [17].…”