Bibliographic record
Abstract
As the system chief nursing officer (CNO) of a large academic health system, I have the responsibility to lead the strategic direction for Cleveland Clinic’s 28 000-member nursing team, including 15 000 bedside nurses, 1600 advanced practice nurses, 610 nurse leaders, and more. The nursing executive team consists of 9 associate chief nursing officers (ACNOs), 14 CNOs, and 4 nursing administration team members. Spearheading our health system’s largest caregiver group (representing nearly half of the health system), I oversee nursing practice, development and education in inpatient, outpatient, rehabilitation, and home care fields throughout the systems’ 165-acre main campus, 15 regional hospitals, 150 northern Ohio outpatient locations (including 18 full-service, family health centers and 3 health and wellness centers), and locations in Weston, Florida, Las Vegas, Nevada, Toronto, Canada, Abu Dhabi, United Arab Emirates, and London, England. I am a member of Cleveland Clinic’s executive leadership team, reporting directly to the president and chief executive officer of our health system. I continually act to help Cleveland Clinic achieve and exceed established financial, operational, and clinical goals that include reducing care costs, making Cleveland Clinic a best-in-class workplace, developing superior clinical care, research, education and innovation, and improving quality, safety, and care experience. This executive role in nursing leadership has positioned Cleveland Clinic nursing as the world leader in nursing excellence.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.319 | 0.107 |
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".