Conectar
Identifying the core information needs of breast cancer radiotherapy patients serves as the foundation for delivering targeted information services. The Kano model, a qualitative tool for classifying service needs, is increasingly being employed to prioritise patient needs and enhance healthcare quality.
This study aims to examine the informational needs of breast cancer patients undergoing radiotherapy using the Kano model as the analytical framework.
Between October 2024 and February 2025, 260 patients with breast cancer undergoing radiotherapy were recruited as study participants. A cross-sectional survey was conducted using the Information Needs Questionnaire. Kano analysis was applied to identify and assess the information needs of these patients. This study adhered to the STROBE guidelines.
Among the 36 items analysed, 15 items (41.7%) were classified as one-dimensional attributes, primarily related to adverse reaction identification and self-management information. 11 items (30.5%) were identified as attractive attributes, mainly concerning the impact of radiation therapy and social–emotional needs five items (13.9%) were must-be attributes, focusing on basic radiotherapy information. Five items (13.9%) were indifference attributes, including the impact of radiotherapy on breast reconstruction, and guidance on image-related concerns during radiotherapy. The quadrant chart findings revealed that 15 needs were predominant in Area I, five in Improving Area II, five in Secondary Improving Area III and 11 in Reserving Area IV.
The information needs of breast cancer radiotherapy patients are diverse. Kano model analysis aids medical staff in developing health guidance and meeting patients' informational needs.
Understanding the differentiated informational needs of patients with breast cancer undergoing radiotherapy provides valuable insights for developing targeted educational interventions, ultimately improving patient engagement and outcomes.
The contributions of patients/members of the public were limited solely to data collection.
The efficacy of radiotherapy and the satisfaction of patients can be significantly improved by adequately addressing their information needs. This process is impeded by the current lack of a comprehensive tool for assessing these needs.
To develop an Information Needs Questionnaire for patients with breast cancer undergoing radiotherapy and to assess its reliability and validity.
The initial item pool for the questionnaire was developed through a literature analysis and semi-structured interviews with 12 patients with breast cancer receiving radiotherapy. The Delphi method was employed to consult 16 experts and the questionnaire content was refined based on expert feedback and item ratings to form the first draft. A pre-investigation was conducted on 30 patients with breast cancer treated with radiotherapy to refine the item expression. From March–October 2024, item analysis, factor analyses, and reliability tests were conducted on 220 patients. This study adhered to STROBE guidelines.
The final questionnaire comprised 36 items. Exploratory factor analysis revealed 5 dimensions, with all item factor loading within their respective dimensions being ≥ 0.4 and no items exhibiting multiple loadings. These five factors accounted for 72.805% of the total variance. The overall content validity index was 0.980, with item-level content validity index ranging from 0.900 to 1.000. The Cronbach's α coefficient for the entire questionnaire was 0.959, and the coefficients for each dimension ranged from 0.786 to 0.958.
The Information Needs Questionnaire demonstrated excellent reliability and validity in patients with breast cancer undergoing radiotherapy. It can effectively guide medical staff to accurately assess the information needs of patients with breast cancer who are undergoing radiotherapy.
Identifying the authentic informational needs of breast cancer patients throughout the entire radiotherapy process is instrumental in enabling medical staff to devise personalised and targeted information support interventions.
A total of 220 participants provided perspectives on their information needs.
To develop a deep learning-based smart assessment model for pressure injury surface.
Exploratory analysis study.
Pressure injury images from four Guangzhou hospitals were labelled and used to train a neural network model. Evaluation metrics included mean intersection over union (MIoU), pixel accuracy (PA), and accuracy. Model performance was tested by comparing wound number, maximum dimensions and area extent.
From 1063 images, the model achieved 74% IoU, 88% PA and 83% accuracy for wound bed segmentation. Cohen's kappa coefficient for wound number was 0.810. Correlation coefficients were 0.900 for maximum length (mean difference 0.068 cm), 0.814 for maximum width (mean difference 0.108 cm) and 0.930 for regional extent (mean difference 0.527 cm2).
The model demonstrated exceptional automated estimation capabilities, potentially serving as a crucial tool for informed decision-making in wound assessment.
This study promotes precision nursing and equitable resource use. The AI-based assessment model serves clinical work by assisting healthcare professionals in decision-making and facilitating wound assessment resource sharing.
The STROBE checklist guided study reporting.
Patients provided image resources for model training.
FreshRSS 