CULTURAL COMPETENCE TRAINING AND EVALUATION AMONG NURSING HOME STAFF
Bibliographic record
Abstract
Abstract The dynamics of cultural and ethnic identity in healthcare has become more salient in the United States due to the growing population diversity. In particular, significant number of nursing home staff are from diverse sociocultural groups, which necessitates cultural competency in nursing homes in order to provide quality care to older adults. Cultural competency in nursing homes is usually analyzed in terms of the interplay between staff members and the older adults, but this study investigated the relational cultural competency between staff members in the workplace. First, cultural competency training on promoting inclusion and respecting diversity, and communication/teamwork was provided to staff members working in a nursing home in Washington, D.C. To evaluate the level of cultural competency post-training, a semi-structured in-depth interview was conducted with 16 nursing home staff. Participants were purposefully selected to capture variation in cultural identity and ethnicity, national origin, work experience and position at the organization. The interviews were conducted individually and audio-recorded after consent was obtained. The audio files were transcribed verbatim and analyzed with the aid of Nvivo12. Findings show that the training was positively perceived as having impacted their outlook of culture and identity in the workplace. Most of the participants were comfortable with working with individuals from different cultures and identified techniques used in communicating when there are language barriers, including utilizing interpreters and translation tools. Participants recommended that organization’s leadership should create more opportunities for intercultural exchange among staff members through potlucks, and recognizing national holidays of staff members.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".