Continuous Quality Improvement: A Shared Governance Model That Maximizes Agent-Specific Knowledge
Bibliographic record
Abstract
Motivate, Innovate, Celebrate: an innovative shared governance model through the establishment of continuous quality improvement (CQI) councils was implemented across the London Health Sciences Centre (LHSC).The model leverages agentspecific knowledge at the point of care and provides a structure aimed at building human resources capacity and sustaining enhancements to quality and safe care delivery.Interprofessional and cross-functional teams work through the CQI councils to identify, formulate, execute and evaluate CQI initiatives.In addition to a structure that facilitates collaboration, accountability and ownership, a corporate CQI Steering Committee provides the forum for scaling up and spreading this model.Point-of-care staff, clinical management and educators were trained in LEAN methodology and patient experience-based design to ensure sufficient knowledge and resources to support the implementation.To date, 61 interprofessional and cross-functional councils have been established.There are 120 quality improvement and patient safety initiatives at various stages of implementation and evaluation.These improvements range from evidence-based practice integration "firsts" to staff-led process and system redesign.The standardization of processes and procedures across CQI council initiatives has spurred development of a variety of best practices and clinical efficiencies.Projects have been replicated up to 14 times across clinical units, and learnings from initial projects have supported scaling-up opportunities.In addition, two evidence-based practice firsts Continuous Quality Improvement: A Shared Governance Model That Maximizes Agent-Specific Knowledge -including the development of an acute oral care assessment tool and guidelines for implementation of oral care clinical neuroscience patients, as well as the utilization of colostrum for oral immune therapy for neonates and infants -have been introduced.Integral to sustained transformation is the clear articulation of expectations regarding system redesign through the eyes of the patient.Professional Scholarly Practice leadership, a robust communication strategy including a real-time, webbased registry program, GEMBA TV, weekly CQI stories and monthly continuous quality improvement reviews have supported the success of the model.The establishment of CQI councils at the unit level including supportive structures and processes helped to embed continuous quality improvement into our organizational culture.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".