Relationships of Work and Practice Environment to Professional Burnout
Bibliographic record
Abstract
BACKGROUND: Research has established clear links between nurses' experience of professional burnout and many qualities of work environments but more work is needed to clarify interrelationships among aspects of complex organizational settings. OBJECTIVE: To test a nursing worklife model that defined structured relationships among professional practice environment qualities and burnout. METHODS: Hospital-based nurses in Canada (N = 8,597) completed an assessment of worklife (Nursing Work Index, NWI) and burnout (Maslach Burnout Inventory-Human Service Scale, MBI-HSS). RESULTS: A causal model was used to confirm the factor structure of the Professional Environment Scale (NWI-PES) on a subset of NWI items and the factor structure of the MBI-HSS. The analysis provided support for a structural model (nursing worklife model) linking the five worklife factors used to define a fundamental role for nursing leadership in determining the quality of worklife regarding policy involvement, staffing levels, support for a nursing model of care, and physician-nurse relationships. The analysis supported a direct path (negatively weighted) from staffing to emotional exhaustion and a direct path (positively weighted) from nursing model of care to personal accomplishment. DISCUSSION: Implications for refining a model of worklife are discussed. Implications for enhancing the quality of worklife and supporting engagement with work are considered.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".