Barriers and facilitators of Canadian quality and safety teams: a mixed-methods study exploring the views of health care leaders
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it