Therapist and client perceptions of empathy in simulated teletherapy sessions
Bibliographic record
Abstract
Empathy is thought to be reduced in videoconference (VC) compared to face-to-face (F2F) therapy. To empirically test this possibility, therapist and client ratings of empathy and its correlates were examined in VC and F2F sessions. Forty-eight students in clinical training programs were randomly assigned to the role of ‘therapist’ or ‘client’, forming 24 dyads. Each dyad completed a simulated clinical session in F2F and in VC and reported on empathy and telepresence. Both therapist and client ratings of empathy were significantly lower in VC compared to F2F. Therapist empathy correlated with telepresence, particularly its physical subcomponent, and therapeutic presence, but these associations were not found for clients. The relationship between empathy and telepresence was investigated further in a real-world study. Twelve clients met with clinical psychology trainees over VC and reported on empathy and telepresence. The associations between therapist empathy, total telepresence, and the physical component of telepresence were replicated. Again, client ratings of empathy and telepresence did not correlate. These results provide the first empirical evidence that empathy is reduced in VC teletherapy compared to F2F and highlight the connection between the degree of empathy felt by therapists and their sense of telepresence during teletherapy.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".