“We’re Looking for Support from Allah”: A Qualitative Study on the Experiences of Trauma and Religious Coping among Afghan Refugees in Canada Following the August 2021 Withdrawal
Bibliographic record
Abstract
In August 2021, the United States withdrew from Afghanistan after 20 years. The fall of the Afghan government to the Taliban resulted in the displacement of some Afghans. Canada committed to welcoming thousands of refugees. Research suggests that refugees tend to have higher rates of post-traumatic stress, and Afghan refugees, in particular, have among the highest rates. Another body of literature suggests that religious coping has positive effects. This paper presents qualitative data from interviews with 11 Afghan refugees who arrived in Ontario after August 2021 with the intent to combine these two findings. In so doing, we sought to understand how Afghan refugees described their experiences of displacement and the extent to which those experiences were traumatic, but also how they relied on Islam to cope with the traumatic effects of displacement. The interviews we conducted suggested that our participants experienced exposure to death, exposure to threat of death and/or injury, and described some of symptoms of the criteria for PTSD. The interviews also suggested that the participants coped using Islamic concepts, beliefs, and rituals. The qualitative data we present provide rich descriptions of the experiences of trauma in the face of displacement and religious coping.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.022 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".