Effective School-Based Interventions for Refugee and First Generation Immigrant Students
Bibliographic record
Abstract
This qualitative study examines effective mental health interventions for first-generation immigrant and refugee students in an Ottawa-based school board. By exploring the experiences of both mental health practitioners and students, the study seeks to identify key considerations for the delivery of mental health counselling services in the school setting. Data was collected through semi-structured interviews with nine practitioners (five psychologists and four social workers) and six students (three first-generation immigrants and three refugees), totaling 15 participants from schools in Ottawa, Ontario. Thematic analysis of the interview data was performed to identify themes that emerged from the data. Several themes were shared by the two groups of participants, whereas others were specific to the student and practitioner groups. A major theme that emerged focused on facilitators and barriers to seeking connection and a sense of belonging. In addition, practitioners reflect on therapeutic practices and identified considering a community based model and capacity of the individual therapist as major factors impacting practice. Though exploratory, the findings offer valuable insights into the unique challenges these students face and reveal facilitators and barriers to the development of effective support systems. The study contributes a culturally relevant perspective to the existing literature, emphasizing the need for tailored approaches when working with first-generation immigrant and refugee students. Recommendations for practice within the context of Ontario schools are discussed. This research provides essential information for mental health professionals aiming to enhance the efficacy of school-based counselling services within the Ontario context.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".