Bibliographic record
Abstract
Purpose The aim of this paper is to describe the intentional and sustained strategy of Providence Health Care to build a culture focused on quality, safety and innovation. Design/methodology/approach Providence Health Care undertook a number of strategies to build a culture that would enable the organization to live its value of Excellence. Objectives were defined and a framework to achieve those objectives was established. Changes were made to the organizational structure to better support change and improvement. Key leadership and governance structures were established to enable the development of culture, monitor performance, identify improvement priorities, and support teams. These efforts were accompanied by active leadership from the Chief Executive Officer, team and staff development programs and standardization of project management methodologies. Findings While the journey is in progress, significant improvements have been accomplished. Examples are provided to demonstrate the depth and breadth of those improvements. The key lessons learned are that culture building requires patience and a comprehensive effort that is sustained over time and changes in administration. Originality/value This paper is of value to organizations interested in building a culture that enables and sustains quality improvement and in the case of a health care organization, one that is integrated from the Board to the bedside.
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 | 0.044 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".