The Roles of Emotion Regulation and Alexithymia in the Relationship Between Sleep and Social Functioning
Bibliographic record
Abstract
Poor sleep quality has been tied to worse social functioning outcomes, including greater loneliness, fewer social interactions, and lower social integration. Other factors likely play a role in the relationship between sleep quality and social functioning. Specifically, alexithymia and emotion regulation may serve as moderators in these relationships. Data for this study came from the Pittsburgh Cold Study 3, a publicly available dataset ( N = 213). Participants completed self-report measures including the Pittsburgh Sleep Quality Index, the Emotional Regulation Questionnaire, Toronto Alexithymia Scale, and four measures of social functioning: Social Network Index, Social Participation Measure, Short Loneliness Scale, and Interpersonal Support Evaluation List for providing support to others. Sleep quality was significantly related to the social functioning variables. Further, the use of the emotion regulation strategy reappraisal significantly moderated the relationship between sleep quality and social participation. Worse sleep quality was related to lower engagement in social activities, only for participants high in use of reappraisal. Additionally, the use of reappraisal significantly moderated the relationship between sleep quality and giving of support. Worse sleep quality was related to less self-reported giving of support to others only for participants high in the use of reappraisal. Results suggest that the use of reappraisal may be an important factor to consider in the relationship between sleep and social functioning. Future work should extend these findings to the general population and a sample of individuals with relevant diagnoses, such as borderline personality or schizophrenia-spectrum disorders.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".