Exploring Story: A Drama Therapy Intervention for Adolescent Immigrants with Depression
Bibliographic record
Abstract
Canada’s immigrant youth is growing; projecting that by 2016 foreign-born adolescents and children of foreign-born parents will comprise 25% of the population. Understanding their mental health patterns and vulnerabilities is pivotal for the creation of initiatives enhancing protective factors, while minimizing the risks related to immigration. Currently, adolescents in Canada have the highest rates of depression in the country. Statistics outlining the prevalence of depression for adolescent immigrants have shown mixed results. But research evaluating trajectories of depressive symptoms demonstrate that they are affected by the same risk and protective factors identified in the general adolescent literature. By employing narrative elements from de Saint-Exupery’s The Little Prince, a unique clinical intervention program is proposed based on drama therapy principles to decrease and/or prevent the development of depressive symptoms. The relevance of core drama therapy factors is discussed (i.e., distancing and projection), along with the importance of employing story, role playing, and action-oriented group interventions. The proposed program aims to address a series of therapeutic objectives: (a) to foster support by exploring client’s support networks; (b) to explore self-identity and autonomy, facilitating individuation processes; (c) to create space for emotional corrective experiences; (d) to develop coping capacities when facing relational conflicts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".