Staff Education of Culturally and Linguistically Appropriate Services (CLAS) Standards to Improve Staff’s Knowledge in Cultural Competency: A Quality Improvement Project
Bibliographic record
Abstract
The objective of this Doctor of Nursing Practice (DNP) project was to develop, implement, and evaluate cultural awareness in a private outpatient mental health care clinic in accordance with the Culturally and Linguistically Appropriate Services (CLAS) standards and by providing cultural awareness education training. Instrumentation included a competence self-assessment checklist adapted with permission from the Central Vancouver Island Multicultural Society. Frequency counts were used to examine the distribution of categorical demographic variables. Repeated measures ANOVA was used to examine whether a change in awareness, and knowledge, was statistically significant. Awareness scores were found to change significantly over time, F (2, 18) = 55.90, p < .001. Knowledge scores were found to change significantly over time, F (2, 18) = 27.0, p < .001. Knowledge increased significantly from pre to post and was sustained at follow-up. For Skills scores, sphericity was found to be violated and therefore the Greenhouse-Geisser correction was used. The scores were found to change significantly over time, F (1.16, 10.47) = 19.60, p <.001. Skills increased significantly from pre to post and were sustained at follow-up. There was a significant improvement in cultural awareness, knowledge, and skills. Potential improvements for the organization include an increased understanding of cultural concepts and increased self-assurance in providing care that is sensitive to patient's cultural needs. Keywords: Culture, cultural awareness, cultural competency, telehealth, CLAS standards
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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 | 0.042 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".