Reflections of methodological and ethical challenges in conducting research during COVID-19 involving resettled refugee youth in Canada
Bibliographic record
Abstract
Research involving migrant youth involves navigating and negotiating complex challenges in order to uphold their rights and dignity, but also all while maintaining scientific rigour. COVID-19 has changed the global landscape within many domains and has increasingly highlighted inequities that exist. With restrictions focusing on maintaining physical distancing set in place to curb the spread of the virus, conducting in-person research becomes complicated. This article reflects on the ethical and methodological challenges encountered when conducting qualitative research during the pandemic with Syrian migrant youth who are resettled in Canada. The three areas discussed from the study are recruitment, informed consent and managing the interviews. Special attention to culture as being part of the study's methodology as an active reflexive process is also highlighted. The goal of this article is to contribute to the growing understanding of complexities of conducting research during COVID-19 with populations which have layered vulnerabilities, such as migrant youth. This article hopes that the reflections may help future researchers in conducting their research during this pandemic by being cognizant of both the ethical and methodological challenges discussed.
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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 | 0.168 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.067 | 0.054 |
| Scholarly communication | 0.026 | 0.005 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.008 | 0.023 |
| 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".