Uncovering the components of therapeutic empathy through thematic analysis of existing definitions
Bibliographic record
Abstract
OBJECTIVES: To identify the components of therapeutic empathy based on a review of existing definitions. METHODS: A search for therapeutic empathy definitions was conducted in two stages. First, a list of empathy definitions from within healthcare contexts was compiled using existing systematic reviews and a database of empathy definitions. The components of those definitions were identified through thematic analysis. Then, forward and backward citation searching (snowballing) of the papers from which those definitions were retrieved was conducted. These papers were randomly sampled and integrated into the analysis until saturation was reached. RESULTS: The searches yielded 3948 definitions of therapeutic empathy. Saturation was reached after analysing 39 individual definitions. Six interrelated components of therapeutic empathy were identified: exploring, understanding, shared understanding, feeling, therapeutic action, and maintaining boundaries. CONCLUSIONS: This study identified six prevailing components of therapeutic empathy that distinguish it from empathy in general. The findings provide a conceptual starting point that can help the field better focus its understanding and use of activities that relate to empathy in practice. PRACTICE IMPLICATIONS: Future practice, research, and education can use the components generated in this study to more consistently define therapeutic empathy, thereby offering potential to improve patient and practitioner outcomes.
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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 | 0.050 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.036 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".