A trauma-informed approach: Temporal reorientation, identity reconstruction and preventing retraumatisation in refugee entrepreneurship support
Bibliographic record
Abstract
Entrepreneurship support services for refugees provide a wide range of services to support venture creation and build resilience. However, mental health supports to address issues of trauma are often not included in these programmes. This is problematic as trauma experienced by refugees can negatively influence elements of entrepreneurship critical for success and the entrepreneurial journey carries a risk of retraumatisation. We propose a framework for a trauma-informed approach to refugee entrepreneurship support that integrates insights from the literature on trauma-informed care. The framework emphasises three key components: temporal reorientation, identity reconstruction and preventing retraumatisation. Temporal reorientation helps refugees reconnect with the present and envision a positive future using tools like mindfulness and bridging practices. Identity reconstruction focuses on developing a cohesive entrepreneurial identity, enabling refugees to rebuild their sense of self through narrative identity work and cultivating a collective entrepreneurial identity within their communities. Preventing retraumatisation involves creating safe, culturally sensitive environments that foster trust while empowering refugees through holistic, peer-supported interventions. This framework offers a novel approach to addressing the unique challenges refugee entrepreneurs face, integrating mental health considerations into entrepreneurship support and paving the way for future research focused on trauma’s impact within entrepreneurship.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".