Teaching students to identify and address significant critical moments in cross-cultural social work practice
Bibliographic record
Abstract
Critical social work practice should transcend across micro, mezzo, and macro levels, and it is a social work educator’s responsibility to foster skills that are contextualized and connected to social justice and anti-oppressive frameworks. Despite the importance of addressing and integrating systemic oppression in cross-cultural social work practice, students struggle with being able to translate their knowing into doing. Using a video case of a client named Glen who is receiving services in an outpatient addiction counseling program, we discuss how to incorporate a critical, systemic, and structural lens in social work mental health practice. Psychotherapy process research cautions against weighing an entire session equally and underlines the importance of examining critical moments within the session. Guided by a psychotherapy process research approach, we illustrate how we train students to identify significant in-session moments and encourage students to brainstorm and practice in-session social work tasks that integrate a structural lens with clinical interventions. This teaching approach also illustrates how to translate critical scholarship and approaches into micro-level teachable moments in classroom to guide students’ cross-cultural practice. The pedagogical approach using a micro-analysis of in-session social work tasks heightens students’ awareness and competence in how to intervene with culturally diverse clients.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".