Getting better and staying better: Assessing civility, incivility, distress, and job attitudes one year after a civility intervention.
Bibliographic record
Abstract
Health care providers (n = 1,957) in Canada participated in a project to assess an intervention to enhance workplace civility. They completed surveys before the intervention, immediately after the intervention, and one year later. Results highlighted three patterns of change over the three assessments. These data were contrasted with data from control groups, which remained constant over the study period. For workplace civility, experienced supervisor incivility, and distress, the pattern followed an Augmentation Model for the intervention groups, in which improvements continued after the end of the intervention. For work attitudes, the pattern followed a Steady State Model for the intervention group, in that they sustained their gains during intervention but did not continue to improve. For absences, the pattern reflected a Lost Momentum Model in that the gains from preintervention to postintervention were lost, as absences returned to the preintervention level at follow-up. The results are discussed in reference to conceptual and applied issues in workplace civility.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".