Challenges, coping responses and supportive interventions for international and migrant students in academic nursing programs in major host countries: A scoping review with a gender lens
Bibliographic record
Abstract
Abstract Background: International and migrant students face specific challenges which may impact their mental health, well-being and academic outcomes, and these may be gendered experiences. The purpose of this scoping review was to map the literature on the challenges, coping responses and supportive interventions for international and migrant students in academic nursing programs in major host countries, with a gender lens.Methods: We searched 10 databases to identify literature reporting on the challenges, coping responses and/or supportive interventions for international and migrant nursing students in college or university programs in Canada, the United-States, Australia, New Zealand or a European country. We included peer-reviewed research (any design), discussion papers and literature reviews. English, French and Spanish publications were considered and no time restrictions were applied. Drawing from existing frameworks, we critically assessed each paper and extracted information related to gender.Results: 114 publications were included. Overall the literature mostly focused on international students, and migration history/status and length of time in country were not considered with regards to challenges, coping or interventions. Females and males, respectively, were included in 69% and 59% of studies with student participants, while those students who identify as other genders were not named or identified in any of the research. Several papers suggest that foreign-born nursing students face challenges associated with different cultural roles, norms and expectations for men and women. Other challenges included perceived discrimination due to wearing a hijab and being a ‘foreign-born male nurse’, and in general nursing being viewed as a feminine, low-status profession. Only two strategies, accessing support from family and other student mothers, used by female students to cope with challenges, were identified. Supportive interventions considering gender were also limited; these included matching students with support services personnel by sex, involving male family members in admission and orientation processes, and using patient simulation as a method to prepare students for care-provision of patients of the opposite-sex.Conclusion: Future research and discussion papers in nursing higher education, especially those regarding supportive interventions, need to address the intersections of gender/gender identity and migration/international status, and also consider the complexity of students’ migration contexts.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 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".