Perception of academic nursing staff toward shared governance
Bibliographic record
Abstract
Shared governance is the set of practices under which faculty members and other staff participates in the decision-making process concerning the operation of their institution and may interfere with the everyday work and activities. The perception of academic nursing staff toward “shared governance” was assessed in a nonexperimental survey research design. Using a nonprobability sampling method, all academic nursing staff had the opportunity to respond to the Collaborative Behavior Scale (CBS) at the pre-implementation of shared governance model. Four universities, including public and private universities were included in the study. Both surveys contained a short demographic section and the Collaborative Behavior Scale CBS adapted from Stickler 1991. Results showed a low level of perception which presented by the collaborative behaviors toward shared governance. Interestingly, commitment to shared governance was neither sufficient nor satisfied during this time. Long experiences with advanced ages for MSc and PhD holders' had significantly higher scores on perception than other groups. The results of the survey indicated that academic staff members recognized shared governance as a process, not a project, and that it takes time to share responsibility, accountability, and authority for faculty members. Overall, Jordanian academic nursing staff have not enough willingness nor commitment to shared governance principles.
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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 | 0.008 | 0.021 |
| 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 | 0.001 | 0.001 |
| 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".