Bibliographic record
Abstract
Increasingly, organizations are experiencing changes as a result of extensive downsizing, restructuring, and merging. In Canada, government-sponsored medicine has been affected as hospitals have merged or closed, reducing essential medical services and resulting in extensive job loss for hospital workers, particularly nurses. Hospital restructuring has also resulted in greater stress and job insecurity in nurses. The escalation of stressors has created burnout in nurses. This study examines predictors of burnout in nurses experiencing hospital restructuring using the MBI-General Survey which yields scores on three scales: Emotional exhaustion, Cynicism, and Professional efficacy. Multiple regressions were conducted where each burnout scale was the criterion and stressors (e.g., amount of work, use of generic workers to do nurses' work), restructuring effects, social support, and individual resources (e.g., control coping, self-efficacy, prior organizational commitment) were predictors. There were differences in the amount of variance accounted for in the burnout components by stressors and resources. Stressors contributed most to emotional exhaustion and least to professional efficacy. Individual resources were more likely to contribute to professional efficacy and least to emotional exhaustion. Stressors and resources accounted for approximately equal amounts of variance in cynicism. Three conclusions were drawn. First, present findings parallel others by showing that individual coping patterns contribute to professional efficacy. Second, emotional exhaustion was found to be the prototype of stress. Third, prior organizational commitment, self-efficacy, and control coping resulted in lower burnout.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".