Using clinical governance levers to support change in a cancer care reform
Bibliographic record
Abstract
PURPOSE: Introducing change is a difficult issue facing all health care systems. The use of various clinical governance levers can facilitate change in health care systems. The purpose of this paper is to define clinical governance levers, and to illustrate their use in a large-scale transformation. DESIGN/METHODOLOGY/APPROACH: The empirical analysis deals with the in-depth study of a specific case, which is the organizational model for Ontario's cancer sector. The authors used a qualitative research strategy and drew the data from three sources: semi-structured interviews, analysis of documents, and non-participative observations. FINDINGS: From the results, the authors identified three phases and several steps in the reform of cancer services in this province. The authors conclude that a combination of clinical governance levers was used to transform the system. These levers operated at different levels of the system to meet the targeted objectives. PRACTICAL IMPLICATIONS: To exercise clinical governance, managers need to acquire new competencies. Mobilizing clinical governance levers requires in-depth understanding of the role and scope of clinical governance levers. ORIGINALITY/VALUE: This study provides a better understanding of clinical governance levers. Clinical governance levers are used to implement an organizational environment that is conducive to developing clinical practice, as well as to act directly on practices to improve quality of care.
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 | 0.047 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".