Gerontology across the professions and the Atlantic: Development and evaluation of an interprofessional and international course on aging and health
Bibliographic record
Abstract
The need for interprofessional teamwork and the global challenges for health care systems of dramatically increasing numbers of older adults have received increased recognition in gerontological and geriatrics education. The authors report on the pilot development of a hybrid course on aging and health for graduate-level health professions students from Norway, Canada, and the United States. International faculty from partnering universities developed, taught, and evaluated the course. Course assignments included online forum postings, reflections, and a problem-based learning group assignment and presentation. Directed readings and discussion included topics related to health care systems and services in the three participating countries, teamwork, and patient-centered care. To evaluate the course, quantitative and qualitative data were collected and analyzed. Results indicate a significant impact on student learning outcomes, including understanding of issues in international aging and health, attitudes and skills in teamwork, and application to clinical practice. This course clearly established the importance of developing innovative interprofessional educational experiences that respond to the increasingly universal impacts of aging populations on health and social care systems around the world.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".