Finding meaning in unfair experiences: Using expressive writing to foster resilience and positive outcomes
Bibliographic record
Abstract
Decades of research have demonstrated that experiencing workplace unfairness can result in profound negative consequences for employees. Integrating conservation of resources theory with meaning-finding perspectives, we argue that engaging in meaning-finding in the aftermath of unfairness can foster state resilience and promote positive outcomes. To promote meaning-finding, we develop and test a new expressive writing intervention (i.e. a guided writing technique that facilitates the processing of negative experiences). Results indicate that the meaning-finding expressive writing intervention is associated with higher resilience than traditional expressive writing. Moreover, resilience mediates the relationship between meaning-finding (vs. traditional) expressive writing and willingness to reconcile, positive relationships with others, and life satisfaction. Theoretically, our findings highlight that engaging in meaning-finding can transform aversive experiences into opportunities to foster resilience and positive outcomes. Practically, meaning-finding expressive writing provides an effective, simple, and cost-effective tool that can be used by employees and counseling programs to promote recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".