Work environment, health outcomes and magnet hospital traits in the Canadian nephrology nursing scene.
Bibliographic record
Abstract
Nephrology, like others areas of health care, is confronting a nursing shortage. Unless action is taken to address nursing shortages, patient care may be negatively affected (American Nephrology Nurses' Association, 2007). Previous studies have been conducted on magnet hospital traits, quality of nursing worklife, empowerment, job satisfaction, burnout, health outcomes, and their influence on nursing retention in Canada. However, there is little research in this area specific to nephrology nursing. This descriptive study examined whether magnet hospital traits, empowerment, and organizational support contribute to Canadian nephrology nurses' job satisfaction, health outcomes, and perceived quality of patient care. A randomly selected sample of 300 nurse members of the Canadian Association of Nephrology Nurses and Technologists (CANNT) was asked to complete a survey consisting of four instruments: The Nursing Work Index (Lake, 2002), the Conditions of Work Effectiveness Questionnaire II (Laschinger, Finegan, Shamian, & Wilk, 2001), the Pressure Management Indicator (Williams & Cooper, 1998), and the Maslach Burnout Inventory (Maslach, Jackson, & Leiter, 1996). There was a 48.1% response rate. Results demonstrated that some aspects of the Canadian nephrology nursing environment were rated quite favourably (e.g., high standards of care are expected; good working relationships with peers), but areas requiring improvement were evident (e.g., assignments that foster continuity of care). Overall, the nurses felt empowered. The results of the Pressure Management Indicator and Maslach Burnout Inventory indicated that nephrology nurses are generally coping well, but that some of them are struggling. Strategies that improve work environments could promote the recruitment and retention of nephrology nurses. Further research in this area is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".