Photo journals with refugee youth: Methodological reflections of conducting research during the pandemic
Bibliographic record
Abstract
The use of visual methods as a research tool has increased worldwide, along with the need to understand the nuanced and contextual benefits, challenges, and risks of their use. Based on participatory approaches, visual methods can offer an adaptable, interactive, and critical way of engaging with refugee young people, making research more accessible and representative. In Montreal, the COVID-19 pandemic forced programs, services and research involving refugee young people to adapt to meet the needs of this population while respecting physical distancing guidelines. Little is known about the strengths and challenges of using visual methods in the context of physical distancing, especially with refugee young people. In this paper, we describe some of the strengths and challenges of using photo journals, a form of visual methods, with refugee young people (11-17 years old) to document their experiences participating in Say Ça!, a Montreal community-based mentoring program, during the pandemic. Six young people participated in photo journals and individual interviews, and 11 volunteers participated in focus group discussions. The journals prompted young people to describe themselves, their favourite moments at Say Ça! and moments when things did not go as planned. In the findings, we describe opportunities and challenges of using photo journals to engage migrant young people in research during the pandemic. Photo journals facilitated building a rapport with young people, overcoming communication challenges, ensuring valid consent throughout the study, and addressing power dynamics between participants and researchers. Challenges included recruitment, confidentiality, and study logistics. In this paper, we present key lessons learned from using photo journals as a method to capture the perspectives of refugee young people. We argue that by including the views of service users, programs may gain a richer understanding of the elements that contribute to refugee young people wellbeing and, ultimately, help improve community-based support for this population in Montreal and other welcome programs.
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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 |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | 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 | 0.285 | 0.269 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.033 | 0.041 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 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".