Self‐distancing promotes positive emotional change after adversity: Evidence from a micro‐longitudinal field experiment
Bibliographic record
Abstract
OBJECTIVE: This research examines changes in emotionality following adverse experiences in daily life. We tested whether daily self-distancing (vs. self-immersing) in reflections on adversity results in positive change in emotionality. Additionally, we probed the "dosage" effect of repeated self-distancing. METHOD: A micro-longitudinal field experiment combined 4-week daily diary and experimental manipulation of perspective during diary-based reflections on adverse experiences to explore the trajectory of change in emotionality. Each day, participants (N = 149) described and reflected on one significant event from that day and rated emotionality. We randomly assigned participants to reflect from a self-immersed or self-distanced perspective. RESULTS: Self-distanced participants showed a change toward positive emotionality while maintaining the same level of negative emotionality, whereas self-immersed participants did not show changes in positive or negative emotionality. We also observed that self-distancing reached its maximum effect ("dosage") for positive emotionality in the third week of the diary. CONCLUSIONS: Repeated self-distanced reflections can promote positive change in emotionality in the face of everyday adversity. Notably, repeated self-distancing effectiveness has a saturation point. In contrast, self-immersed reflections on adversity do not promote positive emotional change. Together, these observations raise the question how the default self-immersed reflection on traumatic experiences impacts personal growth.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".