Volunteer Mentor Experiences of Mentoring Forced Migrants in the United Kingdom
Bibliographic record
Abstract
Research demonstrates the complex nature of supporting forced migrant populations; however, there is almost no research on volunteer experience of supporting forced migrants. This study explored the experiences of volunteer mentors in the United Kingdom. Eight participants were recruited from a single charitable organization. Data were collected using in-depth, semi-structured interviews, and verbatim transcripts were analyzed using Interpretative phenomenological analysis. Four superordinate themes emerged: “paralyzed by responsibility and powerlessness”; “weighty emotional fallout”; “navigating murky boundaries”; and “enriched with hope, joy, and inspiration.” Participants experienced a range of emotions as a result of their mentoring: from distress to inspiration. Findings suggest that focusing on achievable changes helps mentors. The mentoring relationship is hugely important to mentors but also requires careful navigation. The findings suggest that, whilst it is a fulfilling experience, support is required for volunteers mentoring forced migrants. The relative strengths and limitations of the study are considered. Theoretical implications and suggestions for organizations, clinical applications, and future research are provided.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".