Bibliographic record
Abstract
Empathy, broadly defined as the ability to understand the other and to share others' emotions, motivates prosocial behavior and underlies successful interpersonal relations. Dysfunctions in this ability may cause fundamental difficulties in social communication. Empathy has been measured in various ways, from self-report questionnaires to laboratory objective performance tests. Empathic accuracy (EA), i.e., the ability to accurately empathize, is measured using more complex and ecological paradigms, such as asking participants to infer filmed interactions, or having people narrate personal emotional stories then assessing the correspondence between the perceiver and the target of empathy as the criteria for empathic ability. This measure is particularly useful in the study of clinical populations, where deconstructing the multifaceted concept of empathy may contribute to a more complete understanding of specific clinical profiles. This paper presents a scoping review of the literature on EA in clinical populations, and on EA and clinical traits and states in nonclinical or high-risk populations. Following an exhaustive literature search, 34 studies were found eligible to be included in this review. The largest category was studies focused on EA in people with schizophrenia (31%; 11 papers), followed by studies focused on EA in autism spectrum disorders (ASD) and autistic traits in a nonclinical population (22%; 8 papers). Studies were also found on EA and depression tendencies, psychopathy, social anxiety, behavior disorders, and personality disorders, and a few other clinical conditions. The included studies varied on research aims, designs, sample sizes, and male:female ratios. The overall synthesized results suggest that EA is reduced in schizophrenia and ASD. In other clinical populations, the number of studies was very limited. We urge researchers to further examine EA in these less-studied populations. The review reveals a general underrepresentation of female participants in studies on EA in clinical populations. We suggest that future research address understudied clinical populations, such as those diagnosed with psychopathy. Subject, target, and situational variables should also be considered, with special attention to gender differences (and similarities), the association between EA abilities and adaptive functioning, and the study of individuals with clinical conditions as targets, not just observers, in EA tasks.
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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 | 0.014 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".