Factors Influencing the Success of Quality Improvement Teams: A Qualitative Descriptive Single Case Study
Bibliographic record
Abstract
As healthcare organizations started to return to “business as normal” in the post-COVID-19 pandemic era, there was a renewed focus on quality improvement initiatives being undertaken by multidisciplinary teams. Contextual and team-based factors are known to impact upon the success of these teams. A qualitative descriptive single case study research approach was used to explore the perceptions and experiences of members of multidisciplinary quality improvement teams in Halifax, Nova Scotia, Canada. Three focus groups and two interviews (representing five teams) were conducted with a total of 13 participants. Emergent themes from the focus group and interview data included the following: (1) cultivating a culture of continuous learning and growth (sub-themes: personal learning, supports for learning, and learning communities); (2) leading and influencing effectively (sub-theme: physician leadership); (3) working together and enabling partnerships; (4) finding joy and fulfilment in quality improvement efforts; (5) identifying characteristics of successful quality improvement teams; (6) defining quality improvement project success; (7) overcoming barriers and enhancing facilitators to quality improvement project success. Themes were mapped against the Model for Understanding Success in Quality (MUSIQ v2.0) framework and areas for further consideration and future research were identified including the impact of team stability/cohesion and equity diversity and inclusion (EDI) on multidisciplinary teams. Implications for practice, education, and research are provided.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".