Decision-maker roles in healthcare quality improvement projects: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Evidence suggests that healthcare quality improvement (QI) projects are more successful when decision-makers are involved in the process. However, guidance regarding the engagement of decision-makers in QI projects is lacking. We conducted a scoping review to identify QI projects involving decision-makers published in the literature and to describe the roles decision-makers played. METHODS: Following the Joanna Briggs Institute framework for scoping reviews, we systematically searched for all types of studies in English or French between 2002 and 2023 in: EMBASE, MEDLINE via PubMed, PsycINFO, and the Cumulative Index to Nursing and Allied Health Literature. Criteria for inclusion consisted of literature describing health sector QI projects that involved local, regional or system-level decision-makers. Descriptive analysis was performed. Drawing on QI and participatory research literature, the research team developed an inductive data extraction grid to provide a portrait of QI project characteristics, decision-makers' contributions, and advantages and challenges associated with their involvement. RESULTS: After screening and review, we retained 29 references. 18 references described multi-site projects and 11 were conducted in single sites. Local decision-makers' contributions were documented in 27 of the 29 references and regional decision-makers' contributions were documented in 12. Local decision-makers were more often active participants in QI processes, contributing toward planning, implementation, change management and capacity building. Regional decision-makers more often served as initiators and supporters of QI projects, contributing toward strategic planning, recruitment, delegation, coordination of local teams, as well as assessment and capacity building. Advantages of decision-maker involvement described in the retained references include mutual learning, frontline staff buy-in, accountability, resource allocation, effective leadership and improved implementation feasibility. Considerations regarding their involvement included time constraints, variable supervisory expertise, issues concerning centralised leadership, relationship strengthening among stakeholders, and strategic alignment of frontline staff and managerial priorities CONCLUSIONS: This scoping review provides important insights into the various roles played by decision-makers, the benefits and challenges associated with their involvement, and identifies opportunities for strengthening their engagement. The results of this review highlight the need for practical collaboration and communication strategies that foster partnership between frontline staff and decision-makers at all levels.
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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 | 0.090 | 0.241 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.027 | 0.032 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".