Bibliographic record
Abstract
High workload and unpredictable shift end times can contribute to employee turnover, dissatisfaction, and low staff engagement. The aim of this project was to improve nurse and patient satisfaction within a hospital-based outpatient gastrointestinal endoscopy unit while moving from an existing three-shift procedure staffing model to a two-shift model with defined expectations and predictable shift end times. The shift modification led to an 82% decrease in nurse turnover rates after the first 6 months. There was a 12% decrease in the number of nurses calling in ill to work. Nurse satisfaction, compared to 2 years prior, demonstrated 21% improvement related to "having a sense of achievement"; 39% improvement with "being involved in work unit decisions"; 62% decrease in burnout; and 7% improvement in overall satisfaction. The number of nurses attending and presenting at national, regional, and local conferences increased. Furthermore, overall unit patient satisfaction improved by 1.94% (p = .063) between first-quarter 2014 preimplementation data (n = 183) and first-quarter 2015 postimplementation survey data (n = 140). The created shared governance environment supported nurses' involvement in decision-making and creating a new shift model that led to greater staff and patient satisfaction.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.103 | 0.034 |
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".