"Poverty is our Biggest Enemy": Canadian Nursing Students’ International Learning Experiences (ILEs)
Bibliographic record
Abstract
International Learning Experiences (ILEs) have been a cornerstone of global health education for nursing programs throughout the world.Camosun College's Nursing Department (Victoria, BC, Canada) has conducted numerous ILEs in many developed and developing countries for over a decade with only anecdotal evidence to support these rich yet challenging international placements.Thus, the principle objectives of this research aimed to explore the impact of study abroad placements on students' global health knowledge acquisition, and personal and professional growth, in addition to understanding the important perspectives of the host countries.The methodology combined qualitative and quantitative components and employed a global health framework.The data collection tools included focus group discussions, global health themed critical reflections, a survey, and a structured questionnaire.An interpretive description approach guided the analysis.The results revealed the complexity of the students' personal and professional journey as they incorporated global health concepts into their novice practice.Furthermore, health promotion was a critical dimension of the data illuminating student's enhanced knowledge levels of principles of upstream thinking and effective health education strategies.Cultural competence as a key learning outcome fostered complex ethical discussions supporting the concept of cultural comportment.It is hoped that these research findings, coupled with recommendations for best practice, will help inform the debate on the merits and challenges of ILEs, ensuring that vital concepts of global health knowledge and cultural competence are deeply embedded into future international nursing placements.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".