Social Workers as "Cultural Brokers" in Providing Culturally Sensitive Care to Immigrant Families Raising a Child with a Physical Disability
Bibliographic record
Abstract
Although culturally sensitive care is acknowledged as the gold standard in pediatric rehabilitation, very little is known about the social worker’s role in providing culturally sensitive care to immigrant families raising a child with a physical disability. This study draws on in-depth interviews with 45 clinical staff within two pediatric rehabilitation settings. Study findings, which are embedded within a “cultural brokerage” framework, showed that social workers’ understanding of culturally sensitive care involved being aware of their biases and how their own cultural or professional orientation may influence their interaction with patients. These results also highlighted common challenges that social workers encountered in providing culturally sensitive care. These challenges included language barriers, discrepancies between clinicians’ and patients’ cultural orientation, gender and generational differences, lack of knowledge of resources, and difficulties building rapport and trust. Social workers sought to overcome these challenges by working as “cultural brokers” to link immigrant families to resources and to mediate differences between patients’ and clinicians’ cultural orientations. In conclusion, social workers play a critical role in providing culturally sensitive care to immigrant families raising a child with a disability.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".