Promoting forgiveness among co-workers following a workplace transgression: The effects of social motivation training.
Bibliographic record
Abstract
Forgiveness is one construct that is beginning to demonstrate promise as a health and relationship promoter within the workplace. The primary aim of this research was to examine the effects of one psychological intervention (social motivational training) that was developed to promote forgiveness among co-workers. In the first of two studies, workers were randomly assigned to one of two intervention conditions (i.e., job satisfaction training, social motivational training). Participants read a vignette in which they were to imagine themselves as victims of a co-worker transgression. Judgments of responsibility and co-worker forgiveness were then measured at two intervals: before and after training. In Study 2, workers recounted an actual critical incident involving a co-worker transgression, completed a pretraining questionnaire measuring judgments of responsibility, self-image, and forgiveness, received either a one-on-one job satisfaction training or social motivational training session, and completed a post-training questionnaire. Results from both studies indicated that social motivational training enhanced participants’ forgiveness of a hypothetical and actual co-worker. In addition, Study 2 showed an increase in workers’ self-image following social motivational training, suggesting affirmation of the self as a possible mechanism for the effects of social motivational training on forgiveness.
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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 | 0.000 |
| 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 | 0.001 | 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".