Educational experiences and mental health among war- zone immigrants in Toronto
Bibliographic record
Abstract
Previous research suggested that educational engagement may enhance posttraumatic and postmigration adjustment and contribute to overall wellbeing among war-zone immigrants (Stermac et al., 2008). This study examined this further and compared the educational experiences and the health outcomes of immigrant students and non-students who had resided in global war-zones or within areas of extreme civil unrest prior to emigration. Participants in the study (N = 45) were recent immigrants to Toronto, Canada from global war-zone regions in which they had experienced prolonged exposure to traumatic events and who reported many psychological symptoms in the pre-migration environment. Structured interviews and standardized questionnaires were used to obtain information about exposure to stressful events, educational experiences, psychological health and current functioning. Results indicated that while the majority of participants reported good mental health and life satisfaction, students’ self reports of current functioning both in terms of coping with symptoms and in assessments of well-being provided some evidence that students were able to make positive adjustments within their post-migration environments that may be beyond those made by non-students. The results suggest that those engaged in educational programs have good coping abilities for dealing with trauma and posttraumatic symptoms. Results are discussed in terms of the role of educational and community-based interventions in coping with stress-related psychological sequelae and mental health among war-zone immigrants in Toronto. Keyword: War-Zone, Education and Health
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".