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| ID | Type | Description | Link |
|---|---|---|---|
| 1R01NR021386-01 | U.S. NIH Grant/Contract | View source | |
| A545000 | Other Identifier | UW Madison | |
| NUR/FACULTY AFFAIRS/ADMIN | Other Identifier | UW Madison | |
| Protocol Version 1/14/25 | Other Identifier | UW Madison |
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| Name | Class |
|---|---|
| National Institute of Nursing Research (NINR) | NIH |
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The purpose of this clinical trial is to test whether the RESTORE intervention works to reduce nurse burnout, by engaging nursing staff in system redesign to reduce job demands and increase job resources.
Participants in the RESTORE intervention process will be interviewed about:
Participants will also complete surveys of the impact of RESTORE on job demands, job resources, burnout, and work engagement.
Burnout among nurses occurs when there is an imbalance between job demands and resources in the work system, and is associated with poorer nurse health and well-being, workforce issues, and decreased patient safety. Prior efforts to address burnout among nurses have largely utilized individual-level interventions that fail to address work system drivers of burnout, not engaged nurses in the intervention development and implementation process, and lacked scalability to diverse hospital environments. The overarching goal of this research is to decrease nursing staff burnout, which will improve their health and well-being and the quality of care they provide. Our scientific premise is that hospital nursing staff burnout will be reduced by: 1) identifying and addressing context-specific job demands that act as drivers of burnout, and 2) optimizing job resources for nursing staff through ownership over the design and implementation of unit- level solutions targeting burnout drivers. We propose to address these limitations in previous interventions and test the effectiveness of a nurse-led intervention REducing nurse burnout through SysTems analysis and Organizational REdesign (RESTORE) on reducing job demands, increasing job resources, and reducing burnout among hospital unit nursing staff.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Post-RESTORE Implementation | Experimental | All staff working on the unit will be exposed to the outcomes of the RESTORE intervention and will be included in survey and interview data collection post-implementation. |
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| Baseline Pre-Intervention | No Intervention | Data will be collected from nursing staff before RESTORE is implemented on each unit. | |
| RESTORE Design Team | Experimental | Nursing staff will be identified from each unit to participate on the unit system design team. These staff will be directly exposed to the RESTORE intervention through participation in RESTORE sessions. These staff will participate in interviews about their experiences with the RESTORE intervention. |
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| Aim 3 Interview Participants | No Intervention | Participants from four groups (listed below) will participate in interviews to identify ongoing barriers and facilitators that impact scalability of RESTORE. These four groups include: 1) leaders from hospitals that participated in the trial of RESTORE, 2) other leaders from our partner health systems, 3) leaders from other hospitals, and 4) leaders from advocacy or quality organizations. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| RESTORE | Other | RESTORE is a process intervention that combines systems analysis and human-centered design (HCD) approaches to directly engage hospital nursing staff in ongoing organizational redesign to address burnout. Specifically, RESTORE is comprised of multiple in-person sessions with hospital unit design teams. In these sessions, hospital nurses are guided through using a well-known systems analysis model to understand their unit, including what factors act as drivers of burnout, what can be modified, and/or what constraints might be in place regarding system changes. Then, using HCD, nursing staff design, develop, and implement a system redesign solution to address the unique drivers of burnout in their unit that works within their constraints. |
| Measure | Description | Time Frame |
|---|---|---|
| Change in Maslach Burnout Inventory (MBI) | MBI is comprised of 22 items and will be used to assess three dimensions of burnout: Emotional Exhaustion, Depersonalization, and Personal Accomplishment. It is scored on a 7-point Likert scale, where 0 = Never and 6 = Every day. Scores range from 0 to 132 with higher scores indicating greater levels of burnout. | Baseline to 30 months (measured quarterly) |
| Measure | Description | Time Frame |
|---|---|---|
| Change in Areas of Worklife Scale (AWS) | AWS measures six areas of worklife that characterize the job demands and resources in the organizational context of burnout: Workload, Control, Reward, Community, Fairness, and Values. The AWS is comprised of 28 items and a separate score is calculated for each of the six areas of worklife. Scores are reported on a 5-point Likert scale, where 1 = Strongly Disagree and 5 = Strongly Agree. Aggregate score across the areas will be reported rather than 6 different scores. Aggregate score ranges from 28-140 with higher scores indicating a better match between the individual and their work environment, and lower scores indicating a mismatch. |
| Measure | Description | Time Frame |
|---|---|---|
| Aim 3 Qualitative Interviews - Barriers and Facilitators to Scalability and Dissemination of RESTORE | The study team will conduct interviews with 4-5 leaders from four groups (n=16-20 total participants): 1) leaders from hospitals that participated in the clinical trial, 2) other leaders from participating health systems, 3) leaders from other hospitals, and 4) leaders from advocacy or quality organizations. Interviews will focus on identifying the needs and potential barriers or facilitators to implementation of RESTORE in new settings, required adaptations to fit RESTORE to local settings and priorities, and required elements of the RESTORE implementation package. Interview transcripts will be analyzed using directed content analysis to identify themes. |
Aim 1 and Aim 2:
Inclusion Criteria:
Exclusion Criteria:
Aim 3:
Inclusion Criteria:
- Hospital and system leaders from one of the following groups
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| Name | Affiliation | Role |
|---|---|---|
| Linsey Steege, PhD | University of Wisconsin, Madison | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Wisconsin | Madison | Wisconsin | 53705 | United States |
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| ID | Term |
|---|---|
| D000077062 | Burnout, Psychological |
| D000073397 | Occupational Stress |
| ID | Term |
|---|---|
| D013315 | Stress, Psychological |
| D001526 | Behavioral Symptoms |
| D001519 | Behavior |
| D009784 | Occupational Diseases |
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| ID | Term |
|---|---|
| C094068 | Restore polishing paste |
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A stepped wedge cluster randomized controlled trial design will be used. We will roll out RESTORE consecutively to the units using a staggered start time, allowing for controlled introduction of the intervention. In this design, units are randomly sequenced into the intervention.
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| Baseline to 30 months (measured quarterly) |
| Change in Utrecht Work Engagement Scale (UWES) | UWES measures nursing staff work engagement. It is a 9-item survey scored on a 7-point Likert scale. 0 = Never and 6 = Always / Every day. Range of scores is from 0 - 54, with lower scores indicating less engagement. | Baseline to 30 months (measured quarterly) |
| Up to 3 months |
| RESTORE Process Design Team Debrief Interviews | Qualitative analysis will be used to identify themes in the experiences of members of the RESTORE unit design teams. Study team members led by one of the MPIs will analyze the data and use a content analysis approach to identify themes that are emergent from the data | Up to 3 months |
| Post-implementation Qualitative Interviews | Qualitative analysis will be used to identify themes in the experiences of nursing staff working on the study units after RESTORE is implemented. Study team members led by one of the MPIs will analyze the data and use a content analysis approach to identify themes that are emergent from the data | Up to 3 months |