COLLABORATIVE INTERNATIONAL EXPERIENTIAL LEARNING IN GERONTOLOGY: AGING GLOBALLY INITIATIVE
Bibliographic record
Abstract
Abstract Population aging is greatly impacting future generations of healthcare providers tasked with improving the healthcare systems they are inheriting. In this presentation, we introduce an innovative example of how students, researchers, educators and community partners from Canada and Scandinavia came together to comparatively explore models of health, social care, and welfare to advance global health. Collaborative international experiential learning in gerontology is an approach that broadens the application of academic theory outside classroom in an international context. It includes authentic reflection, develops transferable skills and strengthens employability. Our network supports future generations of healthcare professionals in becoming global-ready graduates. We highlight a collaborative course called Aging Globally: Lessons from Scandinavia, initiated at Western University, Canada in 2018, and delivered in partnership with OsloMet University (Norway), Karolinska Institutet (Sweden) and seven non-academic partners, such as Socialstyrelsen and Silviahemmet (Sweden) and Cycling without Age (Denmark). Over the past seven years, the course involved 425 students and 24 professors from Health Studies, Occupational Therapy, Physiotherapy, Nursing, and Technology, Science and Design programs. Student outcomes include expended knowledge, cultural competencies, and transferable skills. The course was a catalyst for three curriculum development grants totaling CAD $2 million, enrichment of the curriculum with 57 international internships and 13 exchanges, 12 summer courses, and new research partnerships, lifting international education in gerontology to a new level. Presenters will share experiences, evidence of impact on students, and describe joint efforts to inspire social change and sustainability of improved quality of life and well-being for older adults everywhere.
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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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".