Creating Brave Spaces in Higher Education: A Short Interprofessional Education Exchange to Support Refugees’ Psychosocial Needs
Bibliographic record
Abstract
Potential traumatic experiences, prior, during and after migration, are the most common risk factors consistently associated with higher rates of mental disorders among refugees. The complex nature of refugee social and health challenges requires a holistic and comprehensive approach to achieve health equity. That is why, interprofessional education and collaboration among health and psychosocial care professionals is becoming increasingly crucial to build strong foundations for socially relevant work for refugees. Academic institutions can provide learning activities to advance students’ interprofessional knowledge and competences before they enter the workforce. This study explored higher education students’ experiences and reflections on a short-term interprofessional exchange that aimed to promote mental health and psychosocial support for refugees. Participants were higher education students from Germany, Greece, Sweden, and Spain representing the fields of psychology, occupational therapy, social work, pedagogy, medicine, and nursing. A qualitative study compromising two focus groups carried out at the end and 18 months after the interprofessional exchanges. Thematic analysis resulted in four themes: a) from curiosity to responsible action, b) my cultural humility journey, c) companion stories, and d) brave spaces and a sense of hope. Interprofessional collaboration emerged as a key strategy in protecting human rights and providing equal opportunities in psychosocial support of refugees. Findings highlight the value of short-term interprofessional exchanges for preparing higher education students in health and social care to move and be responsive in intercultural societies and 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".