Implementing an Organization-Wide Quality Improvement Initiative
Bibliographic record
Abstract
With the movement to advance quality care and improve health care outcomes, organizations have increasingly implemented quality improvement (QI) initiatives to meet these requirements. Key to implementation success is the multilevel involvement of frontline clinicians and leadership. To explore the perceptions and experiences of frontline nurses, project leads, and managers associated with an organization-wide initiative aimed at engaging nurses in quality improvement work. To address the aims of this study, a qualitative research approach was used. Two focus groups were conducted with a total of 13 nurse participants, and individual interviews were done with 10 managers and 6 project leads. Emergent themes from the interview data included the following: improving care in a networked approach; driving QI and having a sense of pride; and overcoming challenges. Specifically, our findings elucidate the value of communities of practice and ongoing mentorship for nurses as key strategies to acquire and apply QI knowledge to a QI project on their respective units. Key challenges emerged including workload and time constraints, as well as resistance to change from staff. Our study findings suggest that leaders need to provide learning opportunities and protected time for frontline nurses to participate in QI projects.
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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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".