Dispositional Empathy Among Psychotherapists: A Latent Profile Analysis
Bibliographic record
Abstract
OBJECTIVE: This study examined therapists' dispositional empathy profiles and how they differ based on professional and personal characteristics. METHOD: A total of 376 clinicians was recruited for this study. Dispositional empathy was assessed with the Interpersonal Reactivity Index (IRI). Profiles were generated using latent profile analysis. Predictors of profiles were assessed with multiple self-report questionnaires measuring demographic and professional characteristics, romantic attachment styles, five-factor personality traits and vulnerable narcissism. RESULTS: A four-profile solution was retained with the following proportions: rational empathic (20%), disengaged/detached (10%), empathic immersion (35%) and insecure/self-absorbed (35%). Overall, few relationships were found regarding demographic and professional characteristics. In contrast, significant relationships were found between profile membership and personal characteristics, including avoidant and anxious attachment, agreeableness, conscientiousness, neuroticism, intellect/imagination and vulnerable narcissism. CONCLUSION: The findings show that differences in therapists' empathic dispositions are linked to personality dimensions. Implications for psychotherapy research, practice and training 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 | 0.003 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".