Perceptions of the critical cultural competence of registered nurses in Canada
Bibliographic record
Abstract
Cultural diversity often leads to misunderstandings, clashes, conflicts, ethnocentrism, discrimination, and stereotyping due to the frequent intersection of many variables, such as differences in traditions, behaviours, ethical and moral perspectives, conceptions of health and illness, and language barriers. The root of the issue is related to the way people conceptualise differences and the unique cultural and historical circumstances that have shaped different groups’ heritages. In this study, therefore, we aimed to investigate the perceptions of critical cultural competence (CCC) of registered nurses working in various hospitals across the province of British Columbia, Canada. Data were collected using Almutairi’s Critical Cultural Competence Scale (CCC Scale) with a random sample of 170 registered nurses. This scale measures four essential multidimensional components of the CCC model: critical awareness, critical knowledge, critical skills, and critical empowerment. Data were analysed using descriptive and inferential statistics (Kruskal-Wallis test). The data revealed that participants’ perceptions of CCC were positive with a mean score of 5.22 out of 7.00 for the total number of items ( n = 43) and a standard deviation of 0.54. The mean scores for the CCC subscales ranged from 4.76 (for critical skills) to 5.42 (for critical empowerment). The results indicated a statistical difference in CCC perceptions based on participants’ age and country of birth with p = 0.05 < 0.05 and 0.029 < 0.05, respectively. Nurses’ age (experience) and country of birth may influence their perceptions of CCC as gaining cultural competence requires exposure to caring for patients from various cultures and countries, and is associated with cultural knowledge and awareness. Therefore, this finding reveals that healthcare organizations must provide ongoing cultural education programs to increase their nursing staff’s level of cultural competence so they are better able to deal with the difficulties that might arise during cross-cultural interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".