Teacher-youth inter-informant agreement on the Strengths and Difficulties Questionnaire (SDQ) in a community sample of refugee and immigrant adolescents in Montreal
Bibliographic record
Abstract
Early detection and treatment of psychological problems amongst refugee and immigrant youth is crucial to improve their developmental outcomes and their social integration. Multiple informants approach is a standard practice in detecting psychological problems. Based on its extensive empirical use, this approach is recommended in order to avoid bias or misinterpretation during the assessment of the youth mental health needs. However, very few studies have investigated the inter-informant agreement between teachers and youth in a multiethnic context. The current study explores teacher-youth inter-informant agreement in a culturally heterogeneous sample of refugee and immigrant adolescents from three high schools in Montreal. The Strength and Difficulties Questionnaire (SDQ) was administered to 113 recently arrived migrant adolescents (female, n = 55; male, n= 58) to assess their own emotional and behavioural symptoms. The SDQ was also administered to their teachers (n = 7) so that a comparison between self- and teacher reports could be made. Teacher-youth agreement was significant for the Emotional symptoms subscale, but this inter-informant agreement was significant for males only. These results underscore the need to raise school personnel awareness about potential undetected emotional problems in newly arrived refugee and immigrant female adolescents.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".