Nursing Research Proposal Example: A Complete Sample With Notes

A good nursing research proposal example shows you more than the headings. It shows how each section argues for the study and how the sections connect. Most samples online give you a list of headings and a few sentences under each. That is not enough when your committee or module leader wants to see a sample size calculation, a data analysis plan and an ethics section that would pass an IRB.

This page gives you a complete quantitative proposal on nurse-led discharge education for heart failure patients, with a short note after each section explaining why it is written that way. Below it is a shorter qualitative example, a comparison of UK and US expectations, and the mistakes that most often get proposals sent back.

On this page:

1. What a nursing research proposal includes
2. Full annotated quantitative example
3. Short qualitative example
4. UK vs US differences
5. Common mistakes
6. FAQs

If you have not chosen a topic yet, start with our lists of patient safety research topics, infection control research topics or critical care nursing research topics, then come back here to build the proposal.

What a Nursing Research Proposal Includes

A nursing research proposal is a plan for a study you have not done yet. It has to convince three readers: your supervisor that the question matters, your ethics board that participants are protected, and yourself that the study can be finished in the time you have.

Most programs expect these sections, in roughly this order:

1. Title and abstract. The title names the intervention or phenomenon, the population and the setting.
2. Background and problem statement. Why the problem matters, with current figures, and the gap your study fills.
3. Purpose, research question and hypotheses. Quantitative studies usually frame the question in PICOT format.
4. Literature review. A synthesis of what is known, organized by theme rather than study by study.
5. Theoretical framework. The theory that explains why your intervention should work or how you will interpret findings.
6. Methodology. Design, setting, sample and sample size, instruments, data collection and data analysis.
7. Ethical considerations. Consent, risk, confidentiality and approvals.
8. Limitations, timeline and budget. What could weaken the findings, how long each stage will take and what it will cost.
9. References and appendices. Consent forms, instruments and recruitment materials.

Check your own program handbook before you start. Some programs merge the literature review into the background, and many UK programs drop the budget entirely.

Nursing Research Proposal Example: Quantitative (Full Sample)

The proposal below is an original sample written for this page. The hospital is fictional, but every statistic and source is real and linked. A note after each section explains the choices behind it.

Title

Effect of Nurse-Led Teach-Back Discharge Education on Self-Care and 30-Day Readmission in Adults With Heart Failure: A Randomized Controlled Trial

Why it works: The title names the intervention (teach-back discharge education), the outcomes (self-care, readmission), the population (adults with heart failure) and the design (RCT). A reviewer knows what the study is before reading a word of the abstract.

Abstract

Background: Heart failure readmissions remain high and costly despite national penalty programs. Teach-back discharge education is promising, but existing trials are small and at high risk of bias. Purpose: To test whether nurse-led teach-back discharge education improves self-care and reduces 30-day readmission compared with usual discharge teaching. Methods: A two-arm randomized controlled trial of 160 adults hospitalized with heart failure at a 350-bed community hospital. Self-care will be measured with the Self-Care of Heart Failure Index v7.2 at baseline and 30 days. Readmission will be taken from hospital records. Analysis: ANCOVA for self-care scores, adjusted for baseline. Logistic regression for readmission. Significance: Findings will tell nurse managers whether teach-back is worth building into standard discharge workflows.

Why it works: Each sentence matches a section of the full proposal. Committees often read only the abstract first, so it must be accurate, not aspirational.

Background and Problem Statement

Heart failure is one of the most common reasons older adults are readmitted to hospital. In Medicare data from 2004 to 2006, about 23% of patients discharged after a heart failure admission were readmitted within 30 days (Ross et al., 2010). Since 2012, the Centers for Medicare & Medicaid Services has reduced payments to hospitals with excess heart failure readmissions under the Hospital Readmissions Reduction Program.

Progress has been slow. In a national registry linked to Medicare claims, risk-adjusted 30-day readmission fell only from 20.0% in 2009 to 19.0% in 2012, and just 1 in 70 hospitals reached a 20% relative reduction (Bergethon et al., 2016). Rates fell after the Affordable Care Act passed but then levelled off once the penalty program began (Blecker et al., 2019).

Many readmissions follow gaps in self-care after discharge: missed diuretic doses, unrecognized weight gain, or delayed calls about worsening breathlessness. The 2022 AHA/ACC/HFSA heart failure guideline gives a Class 1 recommendation that patients receive specific education and support for self-care. It does not say which teaching method works best. At the study hospital, discharge teaching is a printed booklet reviewed by the discharging nurse, with no check of the patient’s understanding.

Problem statement: It is not known whether nurse-led teach-back discharge education improves self-care and reduces 30-day readmission compared with usual booklet-based teaching among adults hospitalized with heart failure.

Why it works: The section moves from the size of the problem (national data) to why it persists (policy has plateaued) to the local gap (booklet teaching). It ends with a one-sentence problem statement that the research question answers directly. Every figure has a source no older than the claim it supports.

Purpose, Research Questions and Hypotheses

The purpose of this study is to test the effect of nurse-led teach-back discharge education on self-care and 30-day readmission in adults hospitalized with heart failure.

PICOT question: In adults hospitalized with heart failure (P), does nurse-led teach-back discharge education (I), compared with usual booklet-based discharge teaching (C), improve self-care maintenance scores and reduce all-cause readmission (O) within 30 days of discharge (T)?

• H1: Participants receiving teach-back education will have higher SCHFI v7.2 self-care maintenance scores at 30 days than participants receiving usual care, after adjusting for baseline scores.
• H2 (exploratory): Participants receiving teach-back education will have lower odds of all-cause 30-day readmission than participants receiving usual care.

Why it works: The PICOT question has one measurable element for each letter. The hypotheses are directional and testable. H2 is labelled exploratory because the sample is not large enough to test it properly, which the methods section explains. Being honest about this impresses committees more than overclaiming. See our guide to PICOT questions in nursing for more examples.

Literature Review

Three themes run through the evidence on teach-back for heart failure.

Teach-back is linked to lower readmission. A systematic review pooling three trials of discharge teach-back found 45% lower odds of 30-day readmission (OR 0.55, 95% CI 0.34–0.91) (Oh et al., 2019). A later meta-analysis limited to heart failure pooled six studies and found a similar direction of effect (OR 0.40, 95% CI 0.17–0.94) (Oh et al., 2023).

Teach-back is rarely tested alone. A review of 26 teach-back studies found that all of them combined teach-back with other educational components, making its specific contribution hard to isolate (Yen & Leasure, 2019).

The evidence base is weak. The 2019 review rated its included studies at high risk of selection bias, and the 2023 meta-analysis found that study quality varied, with only two studies at low overall risk of bias (Oh et al., 2019; Oh et al., 2023).

Gap: Few randomized trials have compared teach-back with usual discharge teaching while measuring self-care with a validated instrument. This study addresses that gap.

Why it works: The review is organized by theme, not by study. Each theme ends in a point that builds toward the gap. In a full proposal, each theme would cite more studies and include an evidence table. If your review keeps coming back with edits, our literature review help can help you restructure it.

Theoretical Framework

The study is guided by the situation-specific theory of heart failure self-care (Riegel et al., 2016). The theory describes self-care as three linked processes: self-care maintenance (taking medicines, following diet), symptom perception (monitoring weight and symptoms) and self-care management (responding when symptoms appear). Teach-back targets all three by checking that patients can explain and demonstrate each task before they leave hospital. Better self-care is expected to reduce readmission.

Why it works: The framework explains why the intervention should work, and it matches the outcome instrument. The SCHFI was written by the same theorists to measure the same three processes. Committees look for that alignment.

Methodology

Design: A two-arm, parallel-group randomized controlled trial with 1:1 allocation.

Setting: Two cardiac step-down units at a 350-bed community hospital in the US Midwest.

Population and eligibility:

• Inclusion: Adults aged 18 or older, admitted with a primary diagnosis of heart failure, discharged home, able to speak and read English, and reachable by phone.
• Exclusion: Documented dementia, discharge to a skilled nursing facility or hospice, or listed for heart transplant.

Sample size: The primary analysis compares SCHFI self-care maintenance scores between groups. A power analysis in G*Power 3 for a two-tailed independent-samples test, with a medium effect (d = 0.5), α = .05 and 80% power, requires 64 participants per group. Allowing 20% attrition, the study will enroll 80 per group (N = 160).

Readmission is a secondary, exploratory outcome. Detecting a fall from 22% to 13% (OR 0.55, the effect reported by Oh et al., 2019) with the same α and power would need about 311 participants per group. That is beyond what one site can enroll in a year, so readmission results will be reported as estimates with confidence intervals rather than as a definitive test.

Why it works: This is the section most sample proposals skip. It states the test, effect size, alpha, power and attrition allowance, and it shows the reader why readmission cannot be the primary outcome. If you are unsure how to justify your effect size, see our guides to power analysis and Cohen’s d.

Randomization and blinding: A statistician not involved in recruitment will generate a block-randomized sequence (blocks of 4 and 6) and place allocations in sealed, opaque envelopes. Outcome assessors collecting 30-day data by phone will not know group allocation.

Intervention: Trained unit nurses will deliver two teach-back sessions of about 20 minutes each, on the day before discharge and the day of discharge. Each session covers daily weights, diuretic and other medicines, sodium and fluid limits, and when to call the clinic. After each topic, the patient explains it back in their own words. The nurse re-teaches until the patient explains it correctly.

Control: Usual care: the standard discharge booklet reviewed by the discharging nurse.

Fidelity: Nurses will complete a 1-hour teach-back training with a role-play competency check. A research nurse will observe 10% of sessions with a fidelity checklist.

Instruments:

The SCHFI v7.2 is free to use and available from self-care-measures.com.

Data collection: A research nurse will screen admissions daily, obtain consent and collect baseline data before randomization. A blinded assessor will collect 30-day SCHFI scores by phone. Readmission data will be pulled from records at 30 days.

Data analysis plan:

Analysis will follow the intention-to-treat principle using SPSS. ANCOVA assumptions (normality of residuals, homogeneity of variance and homogeneity of regression slopes) will be checked before interpretation. Missing 30-day scores will be handled with multiple imputation, with a complete-case sensitivity analysis.

Why it works: Each hypothesis has its own named test, and the plan says what happens if assumptions fail or data are missing. ANCOVA is more powerful than comparing change scores because it adjusts for baseline differences. For help matching tests to your questions, see statistical tests in nursing research and our inferential statistics help.

Ethical Considerations

• Approval and registration: The study will be approved by the hospital IRB before recruitment and registered on ClinicalTrials.gov.
• Consent: Written informed consent will be obtained by the research nurse, not the patient’s bedside nurse, to reduce pressure to take part. Participants may withdraw at any time without any effect on their care.
• Risk: The study poses minimal risk. No one is denied standard care, since the control group receives the hospital’s usual teaching.
• Confidentiality: Data will be stored on an encrypted hospital server under study ID numbers, in line with HIPAA. Only the research team will have access to the linking file.
• Equity: After the study, the teach-back materials will be offered to all heart failure patients if found effective.

Why it works: It addresses the questions an IRB asks first: who obtains consent, whether anyone loses access to care, and how data are protected.

Limitations

The study takes place at one hospital, which limits generalizability. Self-care is self-reported and may be influenced by social desirability. Readmission to hospitals outside the regional network may be missed. Bedside nurses cannot be blinded to allocation, so some usual-care patients may receive informal teach-back (contamination).

Timeline

Budget

Why it works: The timeline overlaps recruitment and follow-up, which is realistic. The budget lists every cost, including zero-cost items, so a reviewer can see nothing was forgotten. Your figures will differ by setting.

Nursing Research Proposal Example: Qualitative (Short Sample)

Many nursing programs ask for a qualitative proposal instead. The core sections are the same, but the question, sampling and analysis change. Here is a condensed example on a related topic, so you can compare the two directly.

Title: Nurses’ Experiences of Delivering Heart Failure Discharge Education on High-Acuity Cardiac Units: A Qualitative Descriptive Study

Problem statement: Heart failure guidelines recommend structured self-care education, yet discharge teaching on busy units is often rushed. Little is known about how bedside nurses experience delivering this education, or what helps and hinders them.

Research question: How do registered nurses on cardiac step-down units describe their experiences of delivering discharge education to patients with heart failure?

Note: A qualitative question asks “how” or “what” and names a phenomenon, not variables. There is no hypothesis, because the study explores rather than tests.

Design: Qualitative descriptive design, which stays close to participants’ own words and suits practice questions where the aim is a clear account rather than abstract theory.

Sample and setting: 12 to 15 registered nurses with at least 6 months’ experience on two cardiac step-down units, recruited by purposive sampling to include a range of experience levels and shift patterns. Final sample size will be guided by information power (Malterud et al., 2016): recruitment stops when the data hold enough relevant detail to answer the question.

Note: Qualitative proposals justify sample size by depth, not by a power calculation. Naming a concept such as information power or data saturation, and citing it, shows you understand this.

Data collection: Semi-structured interviews of 30 to 45 minutes, held in a private room or by video call, audio-recorded and transcribed verbatim. The interview guide (in the appendix) opens with “Tell me about the last time you taught a patient with heart failure before discharge.”

Data analysis: Reflexive thematic analysis following Braun and Clarke’s (2006) six phases: familiarization, coding, generating themes, reviewing themes, defining themes and writing up. NVivo will be used to organize codes.

Trustworthiness: Lincoln and Guba’s (1985) criteria will guide rigor: credibility (member checking with five participants), dependability (an audit trail of coding decisions), confirmability (a reflexive journal) and transferability (thick description of the setting). Reporting will follow the COREQ checklist (Tong et al., 2007).

Ethics: IRB approval, written consent, pseudonyms in all transcripts and reports, and a plan to stop an interview if a participant becomes distressed. Because the researcher works on one of the units, interviews will be conducted by a colleague from another unit to reduce pressure to take part.

Note: Insider research is common in nursing. Naming the power issue and how you will manage it is what reviewers look for. If you need help with coding or theme development, our qualitative data analysis help covers thematic analysis in NVivo.

Quantitative vs Qualitative Proposal: Key Differences

UK vs US Nursing Research Proposals

The same study would be written differently for a UK university than for a US one. The table shows the differences students most often get caught out by. Always check your own module guide, since programs vary.

If your UK program asks for a literature-based dissertation, your proposal will replace the methodology section with a search strategy, inclusion criteria, a critical appraisal tool and a synthesis method.

Common Mistakes That Get Proposals Sent Back

1. The research question and the methods do not match. A “does X improve Y” question needs a comparison group. A cross-sectional survey cannot answer it.
2. No sample size justification. “We will recruit 50 participants” with no power analysis or saturation rationale is the single most common methods comment.
3. The analysis plan names software, not tests. “Data will be analyzed using SPSS” tells the reader nothing. Name the test for each hypothesis.
4. Unvalidated or mismatched instruments. Use a published instrument with reported reliability, and make sure it measures the concept in your framework.
5. A literature review that summarizes study by study. Organize by theme and finish each theme with what it means for your study.
6. A framework that is mentioned once and then forgotten. The theory should shape your variables, your intervention and your interpretation.
7. Outdated statistics. Use the most recent data available, and cite the original source rather than a website that repeats it.
8. Overpromising. Claiming a small single-site study will “change practice nationally” invites skepticism. Be specific about what your findings can and cannot show.

If your proposal has already come back with methods or statistics comments, our dissertation data analysis help team can review your design, power analysis and analysis plan before you resubmit.

Get Help With Your Nursing Research Proposal

A sample shows you what a strong proposal looks like. Building your own still means narrowing a topic, finding the evidence, justifying a sample size and matching every hypothesis to the right test. Those are the steps where most students stall.

Our nursing researchers and statisticians can help at whichever stage you are stuck:

• Topic and research question: turning a broad clinical interest into a focused PICOT or qualitative question
• Literature review: searching, appraising and synthesizing the evidence by theme
• Methods and statistics: design, power analysis, instrument selection and a test-by-test analysis plan
• Editing: APA formatting, structure and alignment between sections

For research papers and proposals, see our nursing research paper help. Doctoral students can start with our DNP dissertation help or PhD dissertation help. You can check our pricing or place an order with your program guidelines and deadline.

Use any sample, including this one, as a model for structure and reasoning. Your proposal should be built around your own topic, setting and sources, in line with your university’s academic integrity policy.

Frequently Asked Questions

How long is a nursing research proposal?

It depends on your level and program. Undergraduate proposals are often a few pages, while master’s and doctoral proposals can run to the length of the first three chapters of a dissertation. Your handbook will give a word or page limit.

What is the difference between a research proposal and a dissertation?

The proposal is the plan, written before you collect data, usually in the future tense. The dissertation reports what you actually did and found. In many programs, the proposal becomes the first three chapters of the dissertation after approval.

Does every nursing research proposal need a hypothesis?

No. Quantitative studies that test an effect or relationship usually state hypotheses. Qualitative and descriptive studies use research questions instead, because they explore rather than test.

Is a DNP project proposal the same as a research proposal?

Not quite. A DNP project proposal applies existing evidence to improve practice at a site, usually as quality improvement, rather than generating new knowledge. It focuses on implementation, measures and sustainability. See our DNP project ideas and DNP scholarly project examples, or get support from our DNP project help team. BSN and MSN students can browse our nursing capstone project ideas.

Can I copy this nursing research proposal example?

Use it as a model for structure, not as text to submit. Your committee expects a proposal built on your own topic, setting and literature, and copied text will be flagged by plagiarism software.

References

Bergethon, K. E., Ju, C., DeVore, A. D., Hardy, N. C., Fonarow, G. C., Yancy, C. W., Heidenreich, P. A., Bhatt, D. L., Peterson, E. D., & Hernandez, A. F. (2016). Trends in 30-day readmission rates for patients hospitalized with heart failure: Findings from the Get With The Guidelines-Heart Failure registry. Circulation: Heart Failure, 9(6), e002594. https://doi.org/10.1161/CIRCHEARTFAILURE.115.002594

Blecker, S., Herrin, J., Li, L., Yu, H., Grady, J. N., & Horwitz, L. I. (2019). Trends in hospital readmission of Medicare-covered patients with heart failure. Journal of the American College of Cardiology. https://pubmed.ncbi.nlm.nih.gov/30846093/

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Centers for Medicare & Medicaid Services. (n.d.). Hospital Readmissions Reduction Program (HRRP). https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146

Heidenreich, P. A., Bozkurt, B., Aguilar, D., Allen, L. A., Byun, J. J., Colvin, M. M., et al. (2022). 2022 AHA/ACC/HFSA guideline for the management of heart failure. Circulation, 145(18), e895–e1032. https://doi.org/10.1161/CIR.0000000000001063

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.

Malterud, K., Siersma, V. D., & Guassora, A. D. (2016). Sample size in qualitative interview studies: Guided by information power. Qualitative Health Research, 26(13), 1753–1760.

Oh, E. G., Lee, H. J., Yang, Y. L., & Kim, Y. M. (2019). Effectiveness of discharge education with the teach-back method on 30-day readmission: A systematic review. Journal of Patient Safety. Advance online publication.

Oh, S., Choi, H., Oh, E. G., & Lee, J. Y. (2023). Effectiveness of discharge education using teach-back method on readmission among heart failure patients: A systematic review and meta-analysis. Patient Education and Counseling, 107, 107559. https://www.sciencedirect.com/science/article/pii/S0738399122008254

Riegel, B., Barbaranelli, C., Carlson, B., Sethares, K. A., Daus, M., Moser, D. K., Miller, J., Osokpo, O. H., Lee, S., Brown, S., & Vellone, E. (2019). Psychometric testing of the revised Self-Care of Heart Failure Index. Journal of Cardiovascular Nursing, 34(2), 183–192. https://doi.org/10.1097/JCN.0000000000000543

Riegel, B., Dickson, V. V., & Faulkner, K. M. (2016). The situation-specific theory of heart failure self-care: Revised and updated. Journal of Cardiovascular Nursing, 31(3), 226–235. https://doi.org/10.1097/JCN.0000000000000244

Ross, J. S., Chen, J., Lin, Z., Bueno, H., Curtis, J. P., Keenan, P. S., Normand, S.-L. T., Schreiner, G., Spertus, J. A., Vidán, M. T., Wang, Y., Wang, Y., & Krumholz, H. M. (2010). Recent national trends in readmission rates after heart failure hospitalization. Circulation: Heart Failure, 3(1), 97–103. https://doi.org/10.1161/CIRCHEARTFAILURE.109.885210

Tong, A., Sainsbury, P., & Craig, J. (2007). Consolidated criteria for reporting qualitative research (COREQ): A 32-item checklist for interviews and focus groups. International Journal for Quality in Health Care, 19(6), 349–357.

Yen, P. H., & Leasure, A. R. (2019). Use and effectiveness of the teach-back method in patient education and health outcomes. Federal Practitioner, 36(6), 284–289. https://pmc.ncbi.nlm.nih.gov/articles/PMC6590951