Back

Development and Evaluation of Heart Failure Patients Oral Comfort Assessment Questionnaire

Lu, M.; Yang, y. R.; Li, n. X.; Wang, k. X.; Tao, M.

2025-02-07 nursing
10.1101/2025.02.04.25321648 medRxiv
Show abstract

BackgroundOur objective was to develop a reliable and valid questionnaire to assess the oral comfort status of inpatients with heart failure, providing medical staff with a scientifically grounded tool for evaluating their oral comfort. MethodsA comprehensive pool of questionnaire items was developed through a literature review, qualitative interviews, and expert consultation. Six experts participated in two rounds of consultation to refine the items, followed by a pre-investigation involving 20 patients. Finally, 192 patients treated at Zun Yi Medical University between June and September 2020 were selected for project analysis and to assess the reliability and validity of the questionnaire. ResultsThrough exploratory factor analysis, four common factors were identified, accounting for a cumulative variance contribution rate of 59.611%. The finalized questionnaire comprised 24 items, demonstrating robust reliability and validity. The Cronbachs coefficient was 0.810, indicating high internal consistency, while the Spearman-Brown coefficient was 0.732. The Pearson correlation coefficient was 0.233 (P < 0.05) when compared with the Beck Oral Scoring Method (BOAS table). The content validity index (CVI) for individual items ranged from 0.833 to 1. The overall content validity index (S-CVI/UA) for the questionnaire was 0.875, with an average content validity index (S-CVI/Ave) of 0.979. ConclusionThe oral comfort assessment questionnaire developed in this study for inpatients with heart failure exhibits excellent reliability and validity, establishing it as an effective and valuable tool for assessing the oral comfort status of this patient population.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.