International student mobility in diagnostic radiography: Benefits and pitfalls
Bibliographic record
Abstract
INTRODUCTION: International student mobility (ISM) is increasingly utilised in higher education to allow students the opportunity to engage authentically with cultures outside their own, providing an opportunity for self-growth. These growth opportunities often extend skills such as cultural competency, academic learning and self-efficacy, all of which are important skills for diagnostic radiography graduates. This study explores the motivations, benefits and pitfalls of an ISM program and highlights key considerations for academics considering organising a program within their own university. METHODS: This study utilised a combination of individual and small group interviews to collect data about diagnostic radiography students' motivations, perceived benefits and pitfalls of undertaking ISM. Data were analysed using reflexive thematic analysis and overarching themes were developed. RESULTS AND DISCUSSION: Three themes were developed from the data, challenges and uncertainty, personal growth, and support. Participants undertaking ISM faced challenges such as cultural differences, as well as feelings of uncertainty. Additionally, they highlighted the importance of organisation in mitigating these challenges. Despite the challenges faced, participants reported significant personal growth and success as a result of the program, integral to this success was the support of their peers and academic advisors. CONCLUSION: ISM programs may lead to enhanced employability of diagnostic radiography graduates, with integral skills such as teamwork, communication cultural competence being enhanced in participants. The role of support during ISM is integral to the success of the program. It is imperative for academics organising ISM programs at their institutions to deliberately consider the way in which radiography students are supported both before and during the program to enhance the experience and ensure outcomes are maximised.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".