Narrative Exposure Therapy: An Innovative Short-Term Treatment for Refugees with PTSD – Interview with Dr. Morton Beiser
Bibliographic record
Abstract
ABSTRACTDr. Morton Beiser is a Professor of Distinction in Psychology at Ryerson University, as well as Founding Director and Senior Scientist at the Centre of Excellence for Research on Immigration and Settlement (CERIS) in Toronto. After obtaining his medical degree from the University of British Columbia in 1960, he interned at the Montreal General Hospital, completed residency in Psychiatry at Duke University Medical Centre and pursued post-doctoral training in Psychiatric Epidemiology at Cornell University. Dr. Beiser was appointed as Associate Professor of Behavioural Sciences at the Harvard School of Public Health from 1967 to 1975, before returning to Toronto to assume a David Crombie Professorship of Cultural Pluralism and Health, and professorship in Psychiatry. Given his extensive research experience on immigration and resettlement work, we interviewed Dr. Beiser to gain further insight into how Narrative Exposure Therapy (NET) can be an innovative short-term option to treat refugee patients with post-traumatic stress disorder (PSTD). Dr. Beiser is currently conducting a randomized trial to assess the effectiveness of NET among refugee children and youth in Toronto. RÉSUMÉDr Morton Beiser est un professeur distingué en psychologie à l’Université Ryerson, ainsi que directeur fondateur et scientifique principal au Centre d’excellence pour la recherche en immigration et en intégration (CEREI) de Toronto. Après avoir obtenu son doctorat en médecine à l’Université de la Colombie-Britannique en 1960, il a fait son internat à l’Hôpital général de Montréal, a complété sa résidence en psychiatrie au centre médical de l’Université Duke et a suivi une formation postdoctorale en épidémiologie psychiatrique à l’Université Cornell. Dr Beiser a été nommé professeur agrégé en sciences du comportement à l’École de santé publique de Harvard de 1967 à 1975, avant de retourner à Toronto pour occuper la Chaire David Crombie sur le pluralisme culturel et la santé, et la chaire de psychiatrie. Compte tenu de sa vaste expérience en recherche sur l’immigration et la réinstallation, nous avons interviewé Dr Beiser pour mieux comprendre comment la thérapie d’exposition descriptive (TED) est une option novatrice à court terme pour traiter les patients réfugiés atteints de trouble de stress post-traumatique. À l’heure actuelle, Dr Beiser mène un essai randomisé pour évaluer l’efficacité de TED chez les enfants et jeunes réfugiés de Toronto.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".