Bibliographic record
Abstract
Background and aims: Authentic leadership, engaged teams and clear vision are essential to the effective delivery of quality paediatric critical care services. Strategies for program innovation and sustained improvements are the framework from which leaders build success. Aims: We will describe our extensive 5 year improvement plan and the systematic leadership interventions which successfully achieved our high performance team vision. Methods: Multimodal interventions were designed to invest in our people, build program structure, establish a tangible organizational and industry presence and entrench a culture of excellence and innovation. Strong leadership presence drove an aggressive recruitment strategy (including new graduate nurses) established an inclusive learning and professional development culture, facilitated a team approach to problem-solving and improve interprofessional/interdepartmental relationships. Progress over time was measured by staff engagement surveys and tracking of quality performance data. Results: Improvements in nurse staffing shortfalls were realized with the elimination of RN vacancy rates (previously 25 to 45%) and new hire candidates are now wait-listed for positions. The RN turnover rate has decreased from 9.7% to 6% and 80% of new graduate nurses have been retained in critical care after 5 years. OR cancellations have been reduced by greater that 30% and patient deferrals nearly eliminated. Staff engagement results, previously below organizational averages, are now consistently sustained above organizational norms. Conclusions: The consistent presence of authentic leadership catalyzed our team, capitalized on existing talent and improved the yield of each program improvement intervention. Comprehensive strategies lead by engaged leaders enhanced our capacity to deliver excellent evidenced based service and improve patient, family and system outcomes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.845 | 0.759 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".