Working With the Encounter: A Descriptive Account and Case Analysis of School-Based Collaborative Mental Health Care for Refugee Children in Leuven, Belgium
Bibliographic record
Abstract
Scholars increasingly point toward schools as meaningful contexts in which to provide psychosocial care for refugee children. Collaborative mental health care in school forms a particular practice of school-based mental health care provision. Developed in Canada and inspired by systemic intervention approaches, collaborative mental health care in schools involves the formation of an interdisciplinary care network, in which mental health care providers and school partners collaborate with each other and the refugee family in a joint assessment of child development and mental health, as well as joint intervention planning and provision. It aims to move away from an individual perspective on refugee children's development, toward an engagement with refugee families' perspectives on their migration histories, cultural background and social condition in shaping assessment and intervention, as such fostering refugee empowerment, equality, and participation in the host society. Relating to the first stage of van Yperen's four-stage model for establishing evidence-based youth care, this article aims to engage in an initial exploration of the effectiveness of a developing school-based collaborative mental health care practice in Leuven, Belgium. First, we propose a detailed description, co-developed through reflection on case documents, written process reflections, intervision, an initial identification of intervention themes, and articulating interconnections with scholarly literature on transcultural and systemic refugee trauma care. Second, we engage in an in-depth exploration of processes and working mechanisms, obtained through co-constructed clinical case analysis of case work collected through our practice in schools in Leuven, Belgium. Our descriptive analysis indicates the role of central processes that may operate as working mechanisms in school-based collaborative mental health care and points to how collaborative mental health care may mobilize the school and the family-school interaction as a vehicle of restoring safety and stability in the aftermath of cumulative traumatization. Our analysis furthermore forms an important starting point for reflections on future research opportunities, and central clinical dynamics touching upon power disparities and low-threshold access to mental health care for refugee families.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".