Quality improvement through a leadership program : influential factors to design and implement QI project using the model of improvement
Bibliographic record
Abstract
Background: Quality Improvement (QI) is a systematic approach to making changes that strengthen clinical practices, enhance professional and organisational development, and ultimately improve patient and population health outcomes. The Canadian healthcare system has encouraged QI by financing change and innovation initiatives and spreading ideas through knowledge translation and exchange. Nurses are key caregivers in the hospital setting and they can significantly influence the quality of healthcare. To build knowledge and engagement among front-line staff, a Leadership Program was developed in a Western Canadian hospital. This research aims to understand influential factors for the design and implementation of QI initiatives led by nurses using the Model of Improvement as the framework to guide improvement work. Methods: This case study is a descriptive investigation. Two cases were selected through purposeful sampling, which sought critical cases that would provide rich and thick description of the initiatives. Data collection methods included 14 semi-structured interviews, three participatory observation activities, and source documents. Data analysis was performed through thematic analysis. Participants in this study included the nurse leaders and team members including QI consultants and unit managers, and other healthcare providers impacted by those initiatives. Results: The results showed that the planning phase and the selection of appropriate interventions or tools was challenging for nurse leaders inexperienced in QI. Testing changes using the PDSA cycles was also not well understood by the majority of the participants. Factors such as the engagement of healthcare providers, increased complexity in cases with multi-professional and multi-unit involvement, and the lack of resources or incentives were perceived as barriers by the participants. Conclusions: QI is difficult to achieve as it implies changing organisational culture and requires both resources and support from organisational leadership. Institutional commitment with the support of senior management in parallel with QI competency building of professionals is paramount to facilitate nurses’ QI role and leadership. Because QI initiatives demand a high investment in healthcare providers’ time and institutional resources, their careful planning, organisational support and the evaluation of outcomes are needed in the future.
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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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".