Statelessness, Trauma and Mental Wellbeing: Implications for Practice, Research, and Advocacy
Bibliographic record
Abstract
In 2016, the UNHCR estimated that about 10 million people, globally, were stateless. The issue of statelessness is inextricably linked to the psychosocial wellness and is a crucial mental health factor to consider in the holistic care, protection of stateless persons. There is a dearth of research examining the mental health implications for stateless persons and their exposure to multiple and ongoing rights violations. In addition, there is limited literature on the mental health impacts of statelessness. This paper describes the systematic human rights violations as linked to statelessness and how these contribute to individual trauma and stress – subsequently affecting wellbeing. The paper utilises a trauma-focused approach in understanding statelessness and presents a novel contribution to the research, advocacy and practice interventions addressing statelessness especially when drawing on the link between trauma and systemic rights violations. Findings from the document study reveal statelessness-linked stressors. Historic systematic human rights violations, traumatic events and situations, and previous and daily stressors become mental health burdens and challenges for those experiencing statelessness. Service providers working with stateless persons should be aware of the impact of statelessness on mental health and should refer cases to mental health practitioners who can provide services that reduce socio-emotional distress whilst strengthening resilience and coping strategies. The findings emphasize the promotion of stateless persons psychosocial wellbeing – looking at both curative and preventative strategies, towards the establishment of just and inclusive societies.
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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 | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".