Effects of Director of Care Support on Job Stress and Job Satisfaction among Long-Term Care Nurse Supervisors
Bibliographic record
Abstract
The provision of care for frail older adults in Long-term care settings is challenging. It requires not only specialized knowledge and skills, but also supportive commitment on the part of directors of care to their nurse supervisors (registered nurses and registered practical nurses) and unregulated healthcare staff. In these complex work environments, communication and leadership are critical to staff job satisfaction. Therefore, it is essential that directors of care represent a source of support for their nurse supervisors. The purpose of this multi-site study was to examine the relationships among perceived support from directors of care, and nurse supervisors' job stress and job satisfaction. Forty-five per cent of the total variance in job satisfaction of nurse supervisors was explained by supervisory support, stress and job category (registered nurse vs. registered practical nurse). Greater supervisory support was also associated with reduced job stress. These findings are essential in developing strategies to improve the nurse supervisory role in long-term care settings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".