Health and Social Care Needs of Somali Refugees With Visual Impairment (VIP) Living in the United Kingdom
Bibliographic record
Abstract
PURPOSE: To explore the health and social care needs of Somali refugees with visual impairment (VIP). DESIGN: We conducted a three-phased focused ethnography in collaboration with the Horn of Africa Blind Society (HABS) through all stages from research design to findings dissemination. METHOD: Engaging in participatory research, HABS members (n = 26), service providers (n = 10), and two Somali community groups (n = 8 and n = 7) whose members were sighted (Phase 1) took part in four focus group interviews. Phases 2 and 3 consisted of interviews with Somali refugees with VIP (n = 32) and their informal carers (n = 5). We used framework data analysis methodology. FINDINGS: Four major themes emerged: (1) sociocultural perceptions of blindness and visual impairment, (2) access to services, (3) isolation and insecurity, and (4) mobility. CONCLUSION: Somali people with VIP experience profound unmet social and health care needs related largely to social support, awareness of mobility options, and the stigmatization of visual impairment. Appropriate community outreach may improve access to services and quality of life for Somali people with VIP. Tailored information is needed to increase awareness of mobility and security services. Significant considerations exist when planning discharge from acute care settings to ensure continuity of support.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".