Predicting cynicism as a function of trust and civility: a longitudinal analysis
Bibliographic record
Abstract
AIM: The aim of this study was to examine whether participant views of job resources (i.e. trust and civility) towards their co-workers and supervisors were longitudinally predictive of workplace cynicism, an aspect of burnout. BACKGROUND: Cynicism is a significant predictor of intention to quit among nurses. Social supports are hypothesized to protect workers from becoming increasingly cynical. METHOD: Measures of cynicism, and trust and civility in both co-workers and supervisors were part of a survey completed by a sample of 323 Canadian nurses whose responses were matched across two time-points, 1 year apart. RESULTS: Hierarchical multiple linear regression analyses revealed that co-worker civility enhanced the ability of our regression models to predict cynicism by explaining 1.1% of the variance in cynicism. The addition of co-worker trust, supervisor civility and supervisor trust did not enhance the ability of the models to predict cynicism. CONCLUSION: The results indicated the importance of workgroup civility in diminishing workplace cynicism. IMPLICATIONS FOR NURSING MANAGEMENT: Efforts to reduce burnout may be improved by decreasing cynicism through interventions aimed at increasing workgroup civility.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".