Before any data exist, MN600's Unit 9 evaluation plan gives each RE-AIM dimension a measure, a data source, a collector and a schedule for a ten-week labor unit pilot. Searches like "mn 600 unit 9 assignment example", "mn600 unit 9 sample" and "mn600 unit 9 example" land here.
RE-AIM Evaluation Plan for a Ten-Week Peanut Ball Pilot
[Student Name]
Purdue University Global
MN600: Evidence-Based Practice Project
Unit 9 Assignment
[Instructor Name]
[Date]
The labor unit, roles and counts are composites written as a model document. Frameworks and measures cited are real.
Framework
The plan uses RE-AIM, introduced by Glasgow, Vogt and Boles to judge interventions by reach, effectiveness, adoption, implementation and maintenance rather than by effectiveness alone (Glasgow et al., 1999). A pilot can fail for reasons unrelated to whether the intervention works, such as low reach or poor implementation, and RE-AIM separates those reasons so each can be seen.
Evaluation Table
Reach. Measure: share of eligible labors with a peanut ball placed within 60 minutes of epidural. Numerator: NTSV labors with epidural and a documented ball placement within 60 minutes. Denominator: all NTSV labors with epidural during the pilot. Source: flowsheet row. Collector: charge nurse, day shift. Timing: weekly.
Effectiveness, primary. Measure: the median interval, in minutes, from epidural to full dilation. Population: NTSV labors with epidural reaching complete dilation. Comparison: the six months before the pilot. Source: electronic record timestamps. Collector: perinatal data coordinator. Timing: weekly, displayed on a run chart.
Effectiveness, system measure. Measure: PC-02 cesarean rate. Numerator: NTSV cesarean births. Denominator: NTSV births. Source: the monthly PC-02 abstraction (The Joint Commission, 2024). Collector: perinatal data coordinator. Timing: monthly.
Adoption. Measure: share of labor nurses who place at least one ball during the pilot, reported separately for day and night shifts. Numerator: nurses with at least one documented placement. Denominator: nurses who cared for at least one eligible patient. Source: flowsheet. Collector: clinical nurse specialist. Timing: every two weeks.
Implementation. Measure: fidelity to the bedside sequence. Audit of 10 randomly selected pilot charts each week for correct ball size, turns at least hourly and documented removal. Collector: shift champions. Timing: weekly.
Maintenance. Measure: reach repeated at three months and six months after the pilot ends, without champion support. Collector: charge nurse. Timing: two single measurements.
What Ten Weeks Can Show
With about 120 eligible labors expected during the pilot, the plan is modest about what the data can reveal. Minutes from epidural to complete dilation are measured in every eligible labor, so a shift in the median may be visible on a run chart; six or more weeks in a row falling below the pre-pilot median, or five or more weeks each lower than the last, would suggest a nonrandom change (Perla et al., 2011). The cesarean rate will be displayed and annotated but not judged, because about 120 labors produce roughly 30 to 35 cesareans, too few for a change of a few percentage points to be told apart from chance.
Balancing Measures
Balancing measures check for unintended harm. Patient requests to remove the ball, counted from the flowsheet. Five-minute Apgar scores below 7 among pilot births, compared with the baseline months. Nurse-reported time per placement and per turn, collected by a one-question survey at the end of weeks 2 and 8. A rise in any of these would prompt review at the midpoint meeting.
Testing the Implementation Model's Forecast
The Unit 8 analysis predicted that night-shift uptake would lag because night nurses have the least time and facilitation (Harvey & Kitson, 2016). Splitting adoption and reach by shift tests that forecast directly. If night-shift reach is lower than day-shift reach by more than 15 percentage points at week 4, the champions and the clinical nurse specialist will add night-shift support for the remaining weeks.
Data Quality Checks
Three checks protect the data. First, the perinatal data coordinator compares the flowsheet count of eligible labors each week with the birth log, so that labors missing from the denominator are found. Second, the timestamps for epidural placement and complete dilation are checked in five random charts each week against the nursing notes, because a timestamp entered late can shift the primary outcome by an hour. Third, the baseline months are pulled using the same query as the pilot weeks, so that differences in definition do not appear as change. Any discrepancy is recorded on the run chart as an annotation.
Roles and Time
The charge nurse needs about 15 minutes a week for reach; the data coordinator about two hours a week for timestamps and the monthly abstraction; the champions about 30 minutes a week each for fidelity audits; and the clinical nurse specialist about one hour every two weeks for adoption and the midpoint meeting. Each person agreed to this time before the pilot was proposed, and the nurse manager approved it.
Reporting
Results will be presented to the perinatal practice council at its May meeting as a one-page summary with the run chart and the RE-AIM table. The council will decide whether to adopt, revise or stop the change based on reach, implementation, the primary outcome and the balancing measures, with the cesarean rate reported as context. If the council adopts the change, the maintenance measurements at three and six months will be reported to it as well, since a practice that fades once champions step back has not really been adopted.
References
Glasgow, R. E., Vogt, T. M., & Boles, S. M. (1999). Evaluating the public health impact of health promotion interventions: The RE-AIM framework. American Journal of Public Health, 89(9), 1322-1327. https://doi.org/10.2105/AJPH.89.9.1322
Harvey, G., & Kitson, A. (2016). PARIHS revisited: From heuristic to integrated framework for the successful implementation of knowledge into practice. Implementation Science, 11, 33. https://doi.org/10.1186/s13012-016-0398-2
Perla, R. J., Provost, L. P., & Murray, S. K. (2011). The run chart: A simple analytical tool for learning from variation in healthcare processes. BMJ Quality and Safety, 20(1), 46-51. https://doi.org/10.1136/bmjqs.2009.037895
The Joint Commission. (2024). Specifications manual for Joint Commission national quality measures: PC-02 cesarean birth. https://manual.jointcommission.org
How this MN600 Unit 9 example is structured
Each row is built so a stranger could collect it, which is why numerators and denominators are written out rather than implied. Effectiveness is deliberately modest about what ten weeks can show. With roughly 120 eligible labors in the pilot, the plan states that minutes to complete dilation may show a shift on a run chart, while the cesarean rate will be displayed and annotated but not judged, since ten weeks produce too few cesareans for a change to be told apart from chance. Balancing measures sit beneath effectiveness: patient requests to remove the ball, five-minute Apgar scores below 7 and nurse-reported time per placement. Collectors are named by role and asked for their time in advance. Adoption is split by shift because the Unit 8 diagnosis predicted night-shift lag, so the evaluation checks the model's own forecast.
Get an MN600 Unit 9 example written to your instructions
Send your proposal, the change model you applied and the Unit 9 prompt with its rubric, and name any evaluation framework the course expects. The plan comes back with every measure defined by numerator and denominator, a data source, a named collector role and a schedule. Your first custom sample is free, in 24-48h. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
MN600 Unit 9 questions, answered
What is RE-AIM?
An evaluation framework introduced by Glasgow, Vogt and Boles in 1999 that judges an intervention on its reach, its effectiveness, how widely settings adopt it, how faithfully it is delivered and whether it lasts. It asks whether a program works in real settings and for whom, not only whether it worked in a trial. Many implementation projects use it because it forces attention to uptake and fidelity as well as outcomes.
Why not judge the pilot on cesarean rates?
Because a ten-week pilot on one unit produces too few cesareans for a change to stand out from ordinary variation. A month with a lower rate could easily be chance, and so could a higher one. The plan still reports the rate, with that caution attached, and puts its primary weight on a measure the change should affect sooner, labor duration.
Who should collect evaluation data on a single unit?
People who already touch the data source, asked in advance and given a realistic time estimate. Charge nurses can pull weekly flowsheet counts; a perinatal data coordinator usually abstracts core measures already; unit champions can audit a handful of charts. Naming roles rather than individuals keeps the plan usable if staff change, and it lets approvers see the workload before agreeing.