Workplace value differences as a predictor of healthcare worker professional quality of life: A structural equation modeling approach
Bibliographic record
Abstract
This MSW thesis explores workplace value differences as a predictor of healthcare worker professional quality of life. Secondary data analysis was performed on data previously collected on hospice and palliative care workers in a 2013 Pan-Canadian Study. The fit of a theoretically informed model to the dataset was analysed, exploring empirical support that healthcare worker value differences within the workplace accounts for a significant part of the variance in their levels professional quality of life. In the Pan-Canadian study, measures of professional quality of life included the Professional Quality of Life Scale (ProQoL). Additionally, the Nursing Unit Cultural Assessment Tool (NUCAT) was also used. This study reviews the pertinent literature, articulates the model suggested above by exploring existing evidence and theory, and tested the model fit using a structural equation model. Understanding the impact that workplace factors have on healthcare worker professional quality of life is an important area of research. Further research utilizing structural equation modeling may help increase our understanding of the multi-factorial nature of professional quality of life measures and its impact on workers, patients, and families.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".