Barriers and facilitators of Canadian quality and safety teams: a mixed-methods study exploring the views of health care leaders
Bibliographic record
Abstract
Background: Health care organizations are utilizing quality and safety (QS) teams as a mechanism to optimize care. However, there is a lack of evidence-informed best practices for creating and sustaining successful QS teams. This study aimed to understand what health care leaders viewed as barriers and facilitators to establishing/implementing and measuring the impact of Canadian acute care QS teams. Methods: Organizational senior leaders (SLs) and QS team leaders (TLs) participated. A mixed-methods sequential explanatory design included surveys (n=249) and interviews (n=89). Chi-squared and Fisher’s exact tests were used to compare categorical variables for region, organization size, and leader position. Interviews were digitally recorded and transcribed for constant comparison analysis. Results: Five qualitative themes overlapped with quantitative data: (1) resources, time, and capacity; (2) data availability and information technology; (3) leadership; (4) organizational plan and culture; and (5) team composition and processes. Leaders from larger organizations more often reported that clear objectives and physician champions facilitated QS teams ( p <0.01). Fewer Eastern respondents viewed board/senior leadership as a facilitator ( p <0.001), and fewer Ontario respondents viewed geography as a barrier to measurement ( p <0.001). TLs and SLs differed on several factors, including time to meet with the team, data availability, leadership, and culture. Conclusion: QS teams need strong, committed leaders who align initiatives to strategic directions of the organization, foster a quality culture, and provide tools teams require for their work. There are excellent opportunities to create synergy across the country to address each organization’s quality agenda. Keywords: health services research, qualitative research, surveys, leadership, quality of health care
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".