Improving Cultural Competency: A Patient-Centered Approach to Interprofessional Education and Practice in a Veterans Healthcare Facility
Bibliographic record
Abstract
Background/Objective: Competency in health professions education when separated from culture is a ‘detached mastery’ of a discreet skill; there are no values considered, no human behind the understanding. This can result in an uneven understanding, proficiency, and commitment concerning individuals’ cultural differences. To increase cultural competency and improve care delivery to veterans, health professional students, participated in an interprofessional education immersion with clinical practicum at a Veteran’s Administration primary care clinic.Methods: Fifty-four graduate students from nursing, clinical psychology, pharmacy and social work participated in an interprofessional education course on military culture. Students’ knowledge and attitudes concerning veterans were evaluated at the start and end of the 8-week immersion course.Results: In both the Knowledge Assessment, a 10-item survey covering the core aspects of the course content, and Health Professionals’ Attitudes Toward Veterans Scale, student knowledge and attitudes improved relating to veterans care.Conclusions: Veterans seeking care in veterans’ and civilian facilities require a culturally competent health professional workforce. Interprofessional education coursework specifically focused on veterans and military culture has shown promise in increasing knowledge and compassion in health professional students working with veteran patients.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".