Teaching empathy to mental health practitioners and trainees: Pairwise and network meta-analyses.
Bibliographic record
Abstract
OBJECTIVE: Empathy is a foundational therapeutic skill and a key contributor to client outcome, yet the best combination of instructional components for its training is unclear. We sought to address this by investigating the most effective instructional components (didactic, rehearsal, reflection, observation, feedback, mindfulness) and their combinations for teaching empathy to practitioners. METHOD: Studies included were randomized controlled trials targeted to mental health practitioners and trainees, included a quantitative measure of empathic skill, and were available in English. A total of 36 studies (37 samples) were included (N = 1,616). Two reviewers independently extracted data. Data were pooled by using random-effects pairwise meta-analysis and network meta-analysis (NMA). RESULTS: Overall, empathy interventions demonstrated a medium-to-large effect (d = .78, 95% CI [.58, .99]). Pairwise meta-analysis showed that one of the six instructional components was effective: didactic (d = .91 vs. d = .39, p = .02). None of the program characteristics significantly impacted intervention effectiveness (group vs. individual format, facilitator type, number of sessions). No publication bias, risk of bias, or outliers were detected. NMA, which allows for an examination of instructional component combinations, revealed didactic, observation, and rehearsal were included among the most effective components to operate in combination. CONCLUSIONS: We have identified instructional component, singly (didactic) and in combination (didactic, rehearsal, observation), that provides an efficient way to train empathy in mental health practitioners. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".