Cultural awareness immersion and introduction to social determinants of health: The 360-degree difference
Bibliographic record
Abstract
Background and objectives: To address the need for innovative educational activities which focus on the Asian American population, and focuses on the Social Determinants of Health (SDOH) and culturally competent care, an immersive, interactive 360-degree video simulation-based educational activity (SBE) was implemented by nursing faculty at a Midwest university. The specific aims of the project were to a) assess changes in participants knowledge and skills as related to SDOH, cultural awareness, and health literacy with the Asian American population; b) participant confidence, and c) assess the usefulness, and ease of use, of 360-degree video simulation.Methods: A single group pretest-posttest design was used in this SBE activity with a convenience sample of 83 undergraduate baccalaureate nursing students at one university.Results: The SBE activity improved overall knowledge and confidence. Furthermore, the majority of participants perceived the SBE activity as useful and that the 360-degree video technology was easy to use.Conclusions: The results contribute to limited literature exploring outcomes of 360-degree video simulation with a focus on the Asian American population and SDOH, cultural competence, and health literacy. Implementing such SBE activities into nursing curriculums is critical to address health disparities and increase knowledge and skill in clinical practice for undergraduate nursing students when serving vulnerable and underrepresented populations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".