Visual Methods for Qualitative Research with Older Refugees: Reflection on Strengths and Drawbacks
Bibliographic record
Abstract
Although older refugees have similar characteristics and vulnerabilities as other populations that have benefited from visual research methods, there is scarce evidence of these methods being used for qualitative social work studies with older refugees. In this article, we discuss the application of visual methods for social work research with older refugees for a better understanding of their experiences and challenges, highlighting how it can be evocative, improve discussions by reaching senses that ordinary interviews cannot grasp, avoid re-traumatisation during research, and build resilience and coping in older refugees. We propose that visual methods could address power dynamics that privilege researchers over participants like older refugees, thus empowering and ensuring older refugees control research processes. This can be crystallised by using visual methods in participatory research. We also outline the drawbacks and concerns of using these methods for research with this population. We conclude by presenting a discourse on how visual methods can advance social work knowledge for practice and social change.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| grok | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| opus | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.408 | 0.454 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".