Bibliographic record
Abstract
In today's health care environment of merged organizations, downsizing and restructuring, employees can be experiencing a debilitating syndrome called "layoff survivor syndrome." This syndrome can have a crippling effect on workers and organizations as employees struggle to adapt to the changed working environment. This article represents my self-reflection as a nursing unit manager who personally experienced survivor sickness and witnessed its impact on the unit staff that I was leading at the time. The work of Noer (1993) is explored to clarify the syndrome and describe how the nursing staff and I manifested the syndrome. The writings of Bridges (1991), Brockner (1992) and Noer (1993) provide timely and relevant insights into managing the impact of layoffs and downsizing on those left behind to carry on. Noer (1993) sees the adaptation to the change as the ability to make the psychological shift from the old business paradigm that perpetuated codependency to the new business paradigm of fostering empowered employees. Bridges (1991) takes us a step further in making this psychological shift to adapt to the new work environment by providing a three phase process he calls transitions. The works of these three authors hold an important message for organizations and employees working in environments that abound with constant change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".