Designing effective healthcare quality improvement training programs: Perceptions of nursing and other senior leaders
Bibliographic record
Abstract
Objective: This study focused on best practices for designing effective quality improvement (QI) training programs for nursing leaders and other senior leaders in a health care organization. Background: Leadership models for quality improvement in healthcare are underdeveloped. Challenged by a wide range of professional roles and responsibilities, nursing leaders are less likely than health professional trainees to have formal training in QI or patient safety. We highlight the experiences that nursing leaders, as well as other senior leaders, from a large health care organization had in participating in a quality improvement training program. Methods: Qualitative methods were used to assess senior leaders’ perceptions and recommendations for creating effective QI training programs. Semi-structured interviews with nursing leaders and other senior health care leaders were conducted to explore participants’ experiences with the training program and their perceptions about whether information gained from the program has the potential to transfer into clinical practice. Participants’ pre and post course knowledge tests were also analyzed to assess overall QI knowledge improvement. Results: Findings suggest that QI knowledge gains for nursing leaders and others were strongest for vocabulary and major concepts, as well as specific QI tools. Additionally, nursing leaders overwhelming appreciated the opportunity to design a QI project, where learning how to write a proper aim statement, was among the biggest benefit to this exercise. Participants in the QI course felt that it was a good investment of their time. The training program also served as a helpful reminder of the importance of quality improvement as well as an awareness of the organization’s commitment to it. Nursing leaders recommended that QI training programs be tailored to their level of existing QI knowledge, their availability and preferences for learning styles, as well as interest in translating QI projects to practice for the purpose of sustaining QI interventions. Conclusions: The findings from this research provide strong evidence that senior QI leadership training programs are important investments for health care organizations. Results indicate that nursing leaders and others in leadership positions can be effectively trained, become very knowledgeable about QI terms and skills, and apply these skills to support staff initiatives to improve outcomes.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".