Abstract Background: Cultural competence (CC) is a crucial attribute in attaining quality healthcare service outcomes, mitigating malfunctioning practices, and improving patients’ satisfaction. Studies suggested comprehensive CC assessment requires appraising existing CC tools to measure health workers’ CC in the Ethiopian context. Selecting existing CC tools, identifying sub-constructs, pinpointing demographic characteristics, and evaluating items are the study objectives. Methods: 20 cultural competence tools of 20 to 83 items, 1-5 sub-constructs, and 4–10 Likert-Type rating options were identified, to be rated by eight experts in three groups. Consensus based Standards for the selection of health Measurement INstruments (COSMIN) and test construction literature are used to develop raters rating codes to compute Inter-Raters Reliability (IRR). The first group of three experts was rated to decide the inclusion of CC tools, factors, and demographic information. The second group of three experts selected six CC tools and 65 items. Two experts in the third group further evaluated the selected items. Results: Reliability for the inclusion of CC tools, factors, and demographic variables were found to be 75%–87%, 50%–93%, and 50%–86% respectively. 13 items that violate test construction principles like absoluteness, endorsement, proneness to multiple interpretation, ambiguity, and double barring were excluded. Cultural skill, cultural knowledge, and cultural awareness are the three most common sub-constructs. Cultural sensitivity, cultural desire, and cultural encounters are the least incorporated factors. Gender, age, language, department, patients encounter, education, and cultural diversity training are demographic variables to assess health workers' CC. Conclusions: Items of CC instruments cover cognitive, affective, and skill domains as prominent dimensions of CC tools. The reviewed tools consist of items that violate test construction principles. To conduct CC assessment in the Ethiopian context, apart from refining and validating selected items, generating additional items is recommended.

F1000Research – Method Article