Gerontology across the professions and the Atlantic: Students’ reflective views and voices
Bibliographic record
Abstract
Health professions students need to have increasing exposure to interprofessional and international experience in developing the knowledge and skills needed to work with older adults. As students, the authors explore in this article the significant elements of our learning that took place in a blended Gerontology Across the Professions and the Atlantic course for participants from the United States, Canada, and Norway. These factors focus on the following aspects of this course: (1) weekly online topic discussions and learning experiences, (2) group case studies and presentations, (3) international perspectives, (4) interprofessional perspectives, and (5) the final course seminar in Bergen. The authors end their discussion by sharing sidebar stories of their experiences in this course that brought together the powerful, transformative elements of interprofessional and international insights into the challenges of geriatric care in the future.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".