Social work practice with war-affected children and families: the importance of family, culture, arts, and participatory approaches
Bibliographic record
Abstract
War and armed conflict not only gravely impact individual children, but the entire family system, with the impacts of war further compounded by the complexities of displacement, flight, migration, and resettlement to new contexts. These processes can cause destabilizing ruptures in the social fabric, networks, and services that support and protect children and families, ultimately hindering their potential protective capacities and potentially contributing to negative long-term intergenerational effects. The family plays a vital role in shaping children‘s mental health and well-being in conflict and post conflict settings, and thus the family needs to be accorded greater consideration in designing psychosocial support services for war-affected populations. With growing numbers of war-affected refugees resettling in Canada and the U.S., it is critical that psychosocial programs and interventions address their unique needs, as individuals, families, and communities. Moreover, there is a greater need for culturally responsive practice with war-affected refugee children and families that accounts for the diversity and heterogeneity of their needs and lived experiences. In this Special Issue entitled: “Children of War and their Families: Perspectives on Social Work Practice & Education”, we suggest that factors such as fostering a family approach, allotting careful attention to context and culture, alongside an emphasis on linking the arts and participation with social work practice, can be key social work contributions to research, education, and practice with this important and often overlooked population.
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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 | 0.034 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.036 | 0.053 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".