Therapists’ Lived Experiences of Recognizing and Addressing Microaggressions Towards Their Clients
Bibliographic record
Abstract
Despite training and personal efforts, psychologists and other mental health professionals often engage in microaggressions towards clients during therapy. Microaggressions refer to comments, behaviours, or actions that dismiss and/or belittle aspects of one’s diverse identities (Sue et al., 2007, 2022). These facets of identity include areas such as race, gender, ethnicity, sexual orientation, gender identity, religion, immigration status and disability. Within the context of therapy, microaggressions are particularly problematic: equity denied clients courageously reaching out for support face yet another space where they are insulted, dismissed, and/or rejected. Unfortunately, many studies show that microaggressions negatively impact therapeutic relationships and, in some cases, lead to premature termination of therapy (Carone et al., 2023; Owen et al., 2019). Client-based research shows that discussing microaggressions can mitigate the harm caused. Unfortunately, no studies have explored exactly how microaggressions are addressed in therapy. Further, there is no research on therapists’ perspectives and experiences of engaging in microaggressions towards clients. The purpose of this study was to explore how psychologists recognized and addressed microaggressions they perpetrated in therapy using Smith et al.’s (2022) Interpretative Phenomenological Analysis (IPA) approach. Five Canadian psychologists (registered or on-track for licensure) were interviewed about their experiences. Eight group experiential themes which highlighted participants’ experiences were found: sensing a shift in therapy; calling out the microaggression; the embodied experience of microaggressing; experiencing self-judgement; navigating unknowns in therapy; preparing oneself to address the microaggression; owning up to the microaggression; and, monitoring cues for resolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".