Blue Zone of Ikaria, Greece: An education abroad program for nursing and health-related disciplines
Bibliographic record
Abstract
International education is growing among American students. In the past two decades, the number of students studying abroad has more than tripled. Research has provided evidence that students who participate in study abroad are more likely to have a variety of career prospects and are more aware socially and culturally. In a world where nurses will be providing care for an increasingly diverse population, cultural awareness and improved interaction with people of different cultures is invaluable. A faculty member with education abroad experience at a mid-size university in the southern United States developed a study abroad program for the summer of 2019 to Ikaria, Greece. This program centered around the concept of Blue Zones, areas of the world identified as having the largest population of centenarians, or people that have lived for longer than 100 years. This article outlines the process of development of the program and the course associated with the program. There is information about course description, course objectives, grading procedures, course activities, and a schedule of activities that students participated in while abroad. Student response to this education abroad experience was very positive. Students have reported that they attempt to implement the nine concepts into their everyday lives since returning, and the impact that this program and other education abroad programs has had, is profound.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".