Addressing the empathy deficit: Beliefs about the malleability of empathy predict effortful responses when empathy is challenging.
Bibliographic record
Abstract
Empathy is often thought to occur automatically. Yet, empathy frequently breaks down when it is difficult or distressing to relate to people in need, suggesting that empathy is often not felt reflexively. Indeed, the United States as a whole is said to be displaying an empathy deficit. When and why does empathy break down, and what predicts whether people will exert effort to experience empathy in challenging contexts? Across 7 studies, we found that people who held a malleable mindset about empathy (believing empathy can be developed) expended greater empathic effort in challenging contexts than did people who held a fixed theory (believing empathy cannot be developed). Specifically, a malleable theory of empathy--whether measured or experimentally induced--promoted (a) more self-reported effort to feel empathy when it is challenging (Study 1); (b) more empathically effortful responses to a person with conflicting views on personally important sociopolitical issues (Studies 2-4); (c) more time spent listening to the emotional personal story of a racial outgroup member (Study 5); and (d) greater willingness to help cancer patients in effortful, face-to-face ways (Study 6). Study 7 revealed a possible reason for this greater empathic effort in challenging contexts: a stronger interest in improving one's empathy. Together, these data suggest that people's mindsets powerfully affect whether they exert effort to empathize when it is needed most, and these data may represent a point of leverage in increasing empathic behaviors on a broad scale.
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 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".