The Relationship Between Quality of Sleep and Emotional Empathy
Bibliographic record
Abstract
Abstract. Sleep loss is known to severely disturb individuals’ mood and emotion processing. Here, we tested the hypothesis that quality of sleep is predictive of individuals’ performance on a task evaluating emotional empathy. We tested 34 healthy undergraduate students [19 males, mean (SD) age = 21.82 (3.26) years; mean (SD) education = 14.98 (1.91) years] recruited through the University of Calgary research participation system. We collected objective (actigraphy) and subjective (questionnaires and self-reports) sleep measures to characterize individuals’ sleep quality, and asked participants to solve a computerized emotional empathy task. We first performed a dimensionality reduction analysis on the sleep-related measures, which resulted in six principal components, and then ran a stepwise multiple regression analysis to investigate the sleep measures that best predicted participants’ scores on the emotional empathy task. We found that subjective sleep quality, together with sleep phase, best predicted participants’ empathic sensitivity to negative images while they explicitly evaluated the emotions of others (i.e., direct component of emotional empathy). Also, subjective sleep quality resulted to be the best predictor of participants’ arousal state in response to negative images, which is an implicit manifestation of their empathic experience (i.e., indirect component of emotional empathy). In both cases, lower subjective sleep quality was associated with lower empathic sensitivity to negative stimuli. Finally, sleep duration best predicted average empathic responses to stimuli of all valences, with shorter sleep durations associated with lower average empathic responses. Our findings provide evidence of a significant relationship between individuals’ quality of sleep and their ability to share the emotions experienced by others. These findings may have important implications for individuals employed in professions requiring social interaction and empathic experience coupled with schedules that interfere with nighttime sleep.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".