An international partnership between a Canadian and Brazilian University: A descriptive report
Bibliographic record
Abstract
Post-secondary programs in health are strengthened when curricula and experiences are internationalized. Internationalization encourages students to think, advocate and practice globally; thus, preparing graduates to contribute to health beyond geo-political borders. However, designing and supporting international experiences in undergraduate health curricula can be challenging due to time and resource constraints, costs, language barriers, and collaborative processes within and across organizations; thus, internationalization is not easily achieved. The purpose of this report is to describe an initiative aimed at supporting undergraduate nursing student experiences in an international partnership program between a Brazilian and Canadian university. First, we describe the project. Second, we present the context of the two partnering undergraduate nursing programs and their respective communities. Third, students from each school describe their experiences with the initiative. Fourth, we integrate our experiences with literature to offer three lessons for moving forward using a framework of resource allocation, fair trade learning, and decolonized competencies. Our report is timely as worldwide, universities are eager to build collaborative partnerships with stakeholders to implement internationalization within curricula. Such partnerships can help students learn about health-related experiences for populations living in distinctive social inequality and inequities in accessing health services.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".