| Course | NUR 6053 Catalyst for Quality Improvement in Nursing Education |
|---|---|
| Module | Module 4 |
| Paper type | Quality improvement project plan |
| Length | 1,390 words, about 5 pages plus title and reference pages |
| Format | APA 7 student paper |
| School | American College of Education |
| Program | Ed.S. in Nursing Education |
| Updated | September 2026 |
Free sample paper for NUR 6053 Module 4
Eighteen Percent Gone by December: Applying the Model for Improvement to First-Semester Attrition, With an Aim Statement, a Family of Measures, Three PDSA Cycles and a Run Chart That Can Actually Move
Student Name
American College of Education
NUR6053: Catalyst for Quality Improvement in Nursing Education
Module 4 Assignment
Instructor Name
November 25, 2030
The Problem in Numbers
Over the last eight terms, an average of 18% of students entering our associate degree program left before the end of the first semester, by withdrawal or failure, with a range of 13% to 24%. With 90 students admitted each term, that is about 16 students each time. Exit interviews and advisor notes point to three recurring patterns: students who failed the first unit examination and saw no way back, students whose work or family obligations collided with the clinical schedule, and students who never contacted anyone before withdrawing. In most cases, the first sign of trouble appeared in the first four weeks, and the first contact from the program came after week eight.
The Model and the Aim
The Model for Improvement, set out by Langley et al. (2009), begins by asking the team to state its goal, to decide in advance what evidence would show a change had helped, and to name the changes it believes will help, and it answers that last question through rapid plan-do-study-act cycles in which small changes are tested, measured and adapted. The method was developed in health care but applies to any process, and a first-semester nursing program is a process with inputs, steps and outcomes.
The aim: reduce first-semester attrition from a mean of 18% to 10% or less for the cohort entering in fall 2031, on both campuses, by identifying students at risk within the first three weeks and connecting each to a specific support within seven days. The aim is specific about the outcome, the size of change, the population and the date, and it names the theory of change: earlier contact leads to fewer losses.
Why Early Contact Is the Theory
The theory behind the aim comes from the retention literature as well as from our own exit data. Jeffreys (2015) describes nursing student retention as the result of interacting groups of factors: student profile characteristics, affective factors, among them confidence and a sense of belonging, academic factors like study habits, environmental factors such as work hours, family responsibilities and finances, and professional integration factors such as contact with faculty and peers. Our three patterns map onto that framework closely. The student who failed the first unit examination and gave up is an academic factor compounded by an affective one, since a single low score undermined confidence. The student whose job collided with the clinical schedule is an environmental factor. The student who withdrew without speaking to anyone is a failure of professional integration, since nobody in the program knew them well enough to notice.
Jeffreys also argues that environmental factors often outweigh academic ones in the decision to leave, and that faculty tend to underestimate them because they are invisible in grades. That is why the change ideas below do not rely on examination scores alone to identify risk. A ten-minute conversation about workload and schedule can surface a problem that no quiz will show, while there is still time to adjust a clinical assignment or connect the student with emergency funds.
A Family of Measures
Outcome measure: the percentage of each entering cohort who leave before the end of the first semester. It is the measure that matters, but it produces one data point per campus per term, which means two a semester. At that rate, it would take years to tell a real improvement from ordinary variation. Process measures, which change weekly, will show whether the changes are happening and are therefore the ones the team will watch. The main process measure is the percentage of students flagged as at risk who are contacted by an advisor within seven days, plotted weekly. A second is the percentage of students who complete a week-two check-in. Balancing measures watch for harm elsewhere: faculty and advisor hours spent on the new process, and the pass rate on the first unit examination, which should not fall if support simply keeps weaker students enrolled without helping them learn. An improvement that saves students from withdrawal only to fail them in week twelve is not an improvement.
Three PDSA Cycles
The cycles begin small, as the method intends. Cycle one tests a week-two check-in with one clinical group of eight students on the north campus: each student meets their clinical instructor for ten minutes to discuss workload, schedule conflicts and confidence. Prediction: at least six of eight will complete it and at least one previously unknown risk will surface. The study phase will record completion and what was learned, and the act phase will decide whether to adapt, adopt or abandon. Cycle two, if cycle one works, tests an early-alert rule with all first-semester students on the north campus: any student who scores below 70% on the first weekly quiz, misses a clinical day or is flagged by an instructor in the check-in is referred to an advisor, who must make contact within seven days. Prediction: 90% of flagged students contacted within seven days. Cycle three extends the adapted process to the south campus and adds a specific support for students who fail the first unit examination: a structured recovery plan with a faculty member, drawing on the remediation path introduced earlier in this course. Each cycle lasts two to four weeks, and the next cycle is designed only after the previous one has been studied.
Doing PDSA Properly
The method is often applied badly. A systematic review of 73 published applications of PDSA in health care found that many did not follow its primary features, that under a fifth showed a documented series of repeated cycles, and that among the fully analyzed reports only 15% guided their cycles with numbers collected at least monthly (Taylor et al., 2014). The plan is designed against these failures: each cycle is small, predictions are written before each test, the process measure is collected weekly, and each cycle's study phase is documented on a one-page form before the next begins. The team, the assessment coordinator, one advisor and one first-semester faculty member from each campus, will meet weekly for twenty minutes during the cycles.
Reading the Run Chart
Each week's value of the process measure goes onto a run chart alongside the median. Perla et al. (2011) describe run chart rules that distinguish a signal of real change from random variation, among them a shift, in which six or more successive values all sit on the same side of the median, and a trend, in which five or more successive values keep climbing or keep falling. The team will use these rules rather than reacting to single weeks. A shift upward in the percentage of flagged students contacted within seven days after cycle two would show that the process has changed. Whether the outcome follows will be seen at the end of each term, and the team will set the two term results against the eight-term baseline. If the process improves but attrition does not fall, the theory of change is wrong, and the team will look again at the exit data for causes that early contact does not reach.
Equity, Workload and Sustaining the Gains
Two risks deserve attention before the first cycle begins. An early-alert rule can label students, and a flag that follows a student into every clinical rotation could shape how instructors see them. Flags will therefore be visible only to the advisor and the assessment coordinator, and each one will close when the student and advisor agree the concern is resolved. The team will also compare flag rates by campus and by student group each term, so that the rule does not fall more heavily on some students without reason.
The second risk is that the process depends on the enthusiasm of the people testing it and fades when the cycles end. If cycle three shows that the process measure holds above 90% on both campuses, the check-in and the referral rule will be written into the first-semester course syllabi and the advisors' job descriptions, and the process measure will move onto the program evaluation dashboard with its own trigger, where the committee reviews it with the other process indicators each semester.
References
Jeffreys, M. R. (2015). Jeffreys's Nursing Universal Retention and Success model: Overview and action ideas for optimizing outcomes A-Z. Nurse Education Today, 35(3), 425-431. https://doi.org/10.1016/j.nedt.2014.11.004
Langley, G. J., Moen, R. D., Nolan, K. M., Nolan, T. W., Norman, C. L., & Provost, L. P. (2009). The improvement guide: A practical approach to enhancing organizational performance (2nd ed.). Jossey-Bass.
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 & Safety, 20(1), 46-51. https://doi.org/10.1136/bmjqs.2009.037895
Taylor, M. J., McNicholas, C., Nicolay, C., Darzi, A., Bell, D., & Reed, J. E. (2014). Systematic review of the application of the plan-do-study-act method to improve quality in healthcare. BMJ Quality & Safety, 23(4), 290-298. https://doi.org/10.1136/bmjqs-2013-001862
The NUR 6053 Module 4 assignment instructions
NUR 6053 Module 4 in many sections asks you to take one educational outcome that is not where it should be, such as attrition, licensure results or first-attempt skills performance, and plan how to improve it with a recognized quality improvement method. Expect to describe the problem with data, state an aim, choose measures, propose changes and explain how you will test and monitor them. The Model for Improvement with PDSA cycles is the most common choice, though Lean or Six Sigma tools may be accepted. Prompts vary on whether you must carry out a cycle or only plan it, so check the Canvas instructions. Length usually runs five to seven pages in APA 7, and a run chart or measure table is often welcome.
How this NUR 6053 Module 4 example is built
The paper follows the order of the Model for Improvement. It begins with eight terms of attrition data and the three patterns behind them, then states the aim with its size, population, date and theory of change. A new section ties that theory to Jeffreys's retention framework, showing why environmental and integration factors matter as much as grades. The measures section explains why a twice-yearly outcome cannot guide weekly work and chooses a process measure that can, with balancing measures to catch harm. Three PDSA cycles then scale from one clinical group to both campuses, each with a written prediction. A section drawn from the Taylor review designs safeguards against common PDSA errors, and the run chart section applies shift and trend rules. The paper closes with equity safeguards and a plan to sustain gains.
Reading the NUR 6053 Module 4 rubric
Most of the rubric weight on this module falls on method. Top marks go to an aim statement that is specific, measurable and dated, and to measures that are sorted into outcome, process and balancing types with reasons for each. The next criterion looks at the change plan: small tests, written predictions and a clear study step, which the three cycles are designed to show. Graders also reward correct use of data tools, so the run chart rules are stated from the source rather than guessed. A further criterion usually covers evidence, where a paper earns more by using sources to shape decisions, as the Taylor review does here, than by citing them in passing. Organization, writing and APA 7 formatting carry the last share of points.
NUR 6053 Module 4 help from the desk
The classic error on this assignment is an aim with no number or date, such as reducing attrition or improving pass rates. Close behind is a plan whose only measure is the outcome, which cannot show progress for months. Students also describe one large change as a PDSA cycle, skip predictions, or treat a cycle as a pilot that either succeeds or fails. Run charts get misread when single points are treated as trends. Watch, too, for balancing measures that are missing altogether; improvement in one place can cause harm in another. If your program's outcome is licensure results, clinical placement or something else entirely, the desk can write a custom Module 4 sample built on your data and your instructor's rubric.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official American College of Education document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
More NUR 6053 and Ed.S. in Nursing Education sample papers
- NUR 6053 Module 1: Formative and Summative Assessment
- NUR 6053 Module 2: Validity and Reliability Analysis
- NUR 6053 Module 3: Program Evaluation Plan
- NUR 6053 Module 5: Remediation and Learner Support
- NUR 6053 Module 6: Program Evaluation Report
- NUR 6013 Module 6: Professional Development Plan
- NUR 6023 Module 1: Active Learning Strategy Analysis
- NUR 6023 Module 6: Deep Learning Assessment Plan
- NUR 6043 Module 5: Clinical Judgment Assessment Plan
NUR 6053 Module 4 questions, answered
What does NUR6053 Module 4 usually ask for?
NUR6053 Module 4 in many sections asks you to apply quality improvement methods, such as aim statements, run charts and PDSA cycles, to an educational outcome like attrition or licensure performance. Your classroom's instructions decide the outcome.
Why use process measures if the outcome is what matters?
Outcomes like attrition produce few data points, so it takes a long time to see change. Process measures can be tracked weekly and show whether the changes are actually happening.
How do I read a run chart?
Plot data over time with the median and look for signals such as a shift of six or more points on one side of the median or a trend of five or more points in one direction, rather than reacting to single points.
Where can I find a free NUR 6053 Module 4 sample paper?
This page provides it: a full Module 4 quality improvement paper on first-semester attrition, with an aim statement, measures, three PDSA cycles, run chart rules, margin notes and verified references. It is free to read in full.
What is a balancing measure in an education improvement project?
A balancing measure watches for harm the change might cause elsewhere. In the example, faculty and advisor hours and the first unit examination pass rate are tracked so that keeping students enrolled does not simply move failure later in the program.