Joining revisited in family therapy: discourse analysis of cross‐cultural encounters between a therapist and an immigrant family
Bibliographic record
Abstract
Abstract With increasing diversity in therapeutic dyads, there has been renewed attention to the process of ‘joining’ in cross‐cultural encounters. Inspired by discourse analysis, we conducted a close reading of therapy transcripts between a Pakistani immigrant mother‐daughter dyad and a Canadian white female therapist in an outpatient clinic. Our findings illustrate detailed discursive interactions for joining techniques – selective joining, confirmation, and tracking – (1) where the therapist facilitates joining moments with the family and (2) where the same techniques are used to preclude further exploration of the family's cultural views. Consequently, the joining process is at times limited by the therapist's enactment of her own assumptions about the family's culture. Due to the doxic nature of cultural assumptions, a discursive analysis may help to prevent therapists from silencing their clients’ cultural voices and to be more reflexive of their assumptions, thus promoting joining. Practitioner points Joining in family therapy is a dynamic process The detailed analysis of joining moments can serve as an example for training therapists to examine their moment‐to‐moment responses to culturally diverse clients Therapeutic constructs/techniques (e.g. joining) cannot be decontextualized or conceptualized as apolitical and acultural Critical reflexivity may prevent therapists from unknowingly oppressing culturally diverse clients’ experiences and cultural identities
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".