Supervisory Support, Job Stress, and Job Satisfaction Among Long-term Care Nursing Staff
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of perceived supervisory support provided by registered nursing staff on job stress and job satisfaction among nurse aides (NAs) working in long-term care. BACKGROUND: Job-related stress is a major problem for NAs working in long-term care settings leading to reduced job satisfaction. No studies have used a theoretical framework to study the nature of relationships between immediate supervisors and NAs' job stress and satisfaction. METHODS: Nurse aides from 10 facilities in Ontario (N = 222) completed measures on the supportive capacity of the supervisor (Supportive Supervisory Scale), work stressors (Expanded Nursing Job Stress Scale), and satisfaction (Job Satisfaction Scale). RESULTS: Multiple linear regression analysis supported an adaptation of Cohen-Mansfield's stress-coping model. Thirty-three percent of the total variance in job satisfaction was explained by supervisory support, stress, birthplace, and first language spoken of the NAs. Greater supervisory support was also associated with reduced job stress. CONCLUSIONS: The results suggest that supervisory support for NAs is an important determinant of NAs' job satisfaction.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".