Microskills of broaching and bridging in cross-cultural psychotherapy: Locating therapy skills in the epistemic domain toward fostering epistemic justice.
Bibliographic record
Abstract
Psychotherapy research has shown evidence of positive impacts of broaching cultural differences on the therapy process and outcomes. This increasing body of research also highlights a need to clarify clinical skills of broaching and subsequent bridging to guide therapists in cross-cultural psychotherapy. In articulating microskills to promote broaching and bridging, we critically reflect on cautions against slipping into a technocratic approach that is a mechanical prescriptive skill-based guideline. Using critical theory on epistemic injustice, we examine how to integrate cultural aspects into therapy conversations that are aligned with epistemic and social justice. Drawing from sociolinguistic and critical scholars on language and power, we interrogate epistemic domains of knowledge and power during broaching and bridging in everyday clinical talks. We focus on theorizing and illustrating "how-to-do" broaching and bridging to guide therapists in everyday cross-cultural encounters with selected microskills such as a therapist's self-disclosure, cultural immediacy, and reflective listening. Using case illustrations with detailed transcripts for each skill, we interrogate how a client's epistemic status can be managed in the moment-to-moment conversation between a client and therapist in the continuum from dismissing to deepening the client's experience. A series of detailed case illustrations are intended to guide therapists' self-reflection and/or train therapists toward meaningful cross-cultural work. Lastly, we discuss the implications of broaching and bridging while situating this work as promoting epistemic justice in cross-cultural therapy encounters. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".