Faculty Preparation for Accompanying Nursing Students on International Experiences: Moving Beyond Trial-and-Error
Bibliographic record
Abstract
Many Canadian nursing programs offer international experiences (IEs) as educational opportunities for students. While evidence of pre-departure preparation exists for students, little is known about the preparation of faculty who accompany them. In this qualitative study, semi-structured interviews were conducted with nine novice-to-expert nursing faculty to explore faculty preparation for accompanying nursing students on IEs. The interpretive description design was informed by critical inquiry methods which examined preparation alignment with critical global perspectives. Four themes were interpreted including: the overarching theme of gaining preparation expertise over time, and three main themes of learning on-the-job, discovering the different responsibilities, and learning for-the-job. In the findings, experience was emphasized over formal preparation. Additionally, preparation was complicated by a lack of global health knowledge and a lack of institutional support. Recommendations include moving beyond learning from trial-and-error and moving towards intentional preparation that better considers the experience, knowledge, skills, and attitudes for preparing nursing faculty for IEs.
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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 | 0.045 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".