Bibliographic record
Abstract
Merger as an organizational change has the potential to create turmoil, unrest, and uncertainty among the employees. Despite the shortage of nursing education workforce, global economic hardships have brought on a recent increase in nursing higher education mergers. The focus on integrated operations of the newly merged organization can burden all involved. Financial and business survival factors can create an unintentional oversight of the employee feelings. Nursing education faculty and administrator’s quality of work lives are related to their performance, which ultimately determines organizational performance. The purpose of this qualitative case study was to evaluate the influence of a nursing higher education merger on the quality of work lives of faculty and administrators. Principal results of this research revealed that faculty and administrators perceived the influence of the nursing higher education merger to be negative in the beginning with a transition to a positive influence over 5 years. Challenges in this merger were related to cultural integration and the magnitude of work required for operationalization. Exact timing of transition of the negative influence to positive was not established and needs further research. These results have implications on the nursing higher education institutions planning future mergers. Nursing education leaders must utilize strategies to address the quality of work life factors during the nursing higher education mergers. Implications of maintaining quality of work lives during an organizational change has the potential to address the nursing and nursing education workforce issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".