Addressing Racial Microaggressions and Racial Enactments in Therapy for BIPOC and Immigrant Clinicians
Bibliographic record
Abstract
This chapter examines the experiences of Black, Indigenous, and other People of Color (BIPOC) clinicians with racial microaggressions and corresponding racial enactments in their interactions with clients, colleagues, and agencies. The first author illustrates a case example where, much like many Asian female migrants in North America, she was expected to assume caretaking responsibilities for a white client, and how this racial dynamic in a multicultural Canada represented the clients psychic struggle with being treated as a second-class citizen in his family. The second author recounts her experience of vicarious racial microaggressions when she provided counseling to a trafficking survivor who shares the same Chinese cultural background. The third author discusses how her racial identity as a Black person was initially a barrier to developing a therapeutic relationship with a white client due to the clients prejudgments about Black people as being angry. Grounded in these unique and powerful professional experiences, the authors also offer their collective reflections and lessons learned for other BIPOC clinicians who may face similar challenges in their practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".