Development and psychometric testing of burn inpatient nursing dependency assessment scale.

Abstract

The aim was to develop and psychometrically test the burn inpatient nursing dependency assessment scale (BINDAS). This was a scale development study. This study was conducted in four phases from November 2019 to November 2021. Items were generated and the initial scale was constructed in phase 1. The preliminary evaluation of items was conducted through expert reviews and a pilot study in phase 2. The scale, including item quality, reliability and validity, was validated with 420 individuals in phase 3. The translation of the scale from Chinese to English was performed in phase 4. Content validity was satisfactory. Thirteen items were retained after item analysis, and three factors accounting for 73% of the total item variance were extracted through exploratory and confirmatory factor analyses. Predictive validity with nursing time spent with patients during 24h was also estimated, with r=.66 (p < .01). Receiver operating characteristic analysis was conducted, and an area under the curve of the scale of 0.94 was obtained. Concurrent validity with Barthel index was estimated, with r=-.71 (p < .01). Cronbach's alpha coefficient for scale was .93, and the correlation between raters for total scores was .95. Burn inpatient nursing dependency assessment scale is a psychometrically valid and reliable measurement instrument as well as objective other-rating scale with 12 items for scoring on a four-point scale (0, 1, 2 or 3) and 1 item for scoring on a two-point scale (0 or 2). BINDAS with 13 items was developed in this study. Nurses can give each patient a total score of 0-38. A high score indicates high nursing dependency. The 13-item scale consists of three factors: basic care need, physiological index, and psychology and adaptation. This scale demonstrated satisfactory psychometric properties and can be used to evaluate patient dependency on nurses in burn units and optimize an individual's care plan to achieve efficient staff allocation.