Predictors of Job Satisfaction in Long-Term Facilities
Why this work is in the frame
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Bibliographic record
Abstract
Purpose: The ability of health care organizations to provide quality care depends on its employees. Employers concerned about improving job satisfaction should consider employeesâ perceptions of their jobs. The purpose of this study was to identify the best predictors of job satisfaction within long-term care (LTC) facilities.\nDesign and Methods: A cross-sectional, multi-site, quality of work life (QWL) survey was completed at three independent not-for-profi t LTC facilities in three communities in Ontario, Canada. 1,329 full, part and casual time non-physician staff on active payroll were eligible to participate. A 45-item, self-administered questionnaire collected information on: co-worker and supervisor support; teamwork and communication; job demands and decision authority; characteristics of the organization; patient/resident care; compensation and benefits; staff training and development; overall impressions of the organization; and socio-demographics.\nResults: The eight most important predictors of job satisfaction among LTC staff were: belief that the organization carried out its mission statement; good supervisor social support; being clear about job responsibilities; not being asked to do an excessive amount of work; job classification; good support for training and development; good teamwork; and being satisfi ed that staff contributions are recognized.\nImplications: The findings show that job satisfaction is a multi-dimensional construct. Efforts to improve the quality of work life and job satisfaction, and ultimately the quality of care will therefore require multiple strategies. The importance to the organization of achieving its mission, expectations and employeesâ work responsibilities must be clearly communicated; and good development support and appropriate recognition of contributions need to be provided.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it