Storytelling as a Cultural Treatment Intervention with Syrian Refugee Children
Bibliographic record
Abstract
Since the current war in Syria began in 2011, millions of people have been internally and externally displaced. This has greatly contributed to the increase in refugee numbers throughout the world (UNHCR, 2017). Women and children are more vulnerable because, under certain regimes, they do not have the same agency as adult men (Vieira ,2014). Of further relevance, while adult refugees deal with their own survival, their children may experience physical and/or emotional neglect (Rutter, 2006). Canada is one of the countries currently receiving Syrian refugees (Immigration, Refugees and Citizenship Canada, 2017). This heuristic research paper argues that storytelling is a particularly powerful tool that can greatly benefit refugee children and respond to their specific needs. This is especially true for Syrian children and children of other cultures who have grown up with rich storytelling traditions. I will explore and justify the use of storytelling as a dramatic therapeutic intervention for Syrian refugee children, arguing that it can be beneficial in treating children refugees who have experienced war trauma. I will also explore why this intervention has been so meaningful to my immigration story and my own journey to wellness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".