Reciprocal language style matching: Indicator or facilitator of therapeutic bond
Bibliographic record
Abstract
Objective The tendency to linguistically synchronize is an adaptive and prosocial process observed in verbal and written communication. Research in therapeutic contexts has primarily conceptualized reciprocal language style matching (rLSM; i.e., similarity of function words) as indicating the therapeutic relationship. However, in non-therapeutic contexts, rLSM has been conceptualized as facilitating relationship formation and maintenance. The aim of the present study was to examine if an indication model or facilitation model provided a better explanation for the association between rLSM and the therapeutic bond.Methods Online text-based crisis-counseling sessions (N = 350) with clients in suicidal crisis were coded for rLSM and therapeutic bond. To examine and compare the indication and facilitation models, we used random intercept cross-lagged panel models.Results The association between rLSM and therapeutic bond was better explained by the facilitation model (i.e., rLSM predicting bond) than the indication model (i.e., rLSM co-occurring with bond). However, a model that included (a) rLSM predicting therapeutic bond and (b) the cross-sectional association between therapeutic bond and rLSM was the best fit.Conclusions Our findings indicate that rLSM may play a role in establishing the therapeutic relationship and be reflective of the client-counselor relationship. Implications for counseling practice are discussed.
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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 | 0.008 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".