The Congruence of Nurses in a Czech Hospital with Organizational Work Setting as Related to Organizational Engagement and Perceived Chances to Fulfill One’s Own Professional Aspirations
Bibliographic record
Abstract
The aim of this study was to study how organizational engagement (OE) of nurses is related to the congruence with the work settings as described by the Worklife Model (Leiter ? Maslach 2004) and how the perceived lack of support in the relational aspect of the nurses’ professional role (PRF) is related to the value-fit with the organization. Our concept of OE is concerned more with the employees’ involvement in organizational goals than with their psychological state of mind. A non-experimental survey design was used to test the hypothetical relationships. From the population of 836 nurses at a Czech district hospital, 411 nurses were chosen by stratified selection. A response rate 83% was achieved with respect to the administered questionnaires.There is a strong relationship between OE and total AWS score (0.373, p < 0.01), which supports our hypothesis. The level of autonomy of nurses, expressed by feeling of control over their job activities, has a very strong relationship with OE (0.418, p < 0.01). The regression model proved the strong predictive power of value-fit and autonomy in the worklife for OE of nurses. The perceived lack of support in the relational aspect of the nurses’ professional role has the highest correlation with workload, followed by value-fit and fairness. The model did not prove that PRF is significantly contributing to the value-fit between nurse and the organization. Furthermore, there is no association between PRF and OE. The implications for the management of quality process are discussed.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".