Predictors of Job Satisfaction in Long-Term Facilities
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 employees’ 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 employees’ work responsibilities must be clearly communicated; and good development support and appropriate recognition of contributions need to be provided.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".