| Course | NR 307A Advancing Equity, Inclusion, and Diversity in Healthcare |
|---|---|
| Week | 3 |
| Paper type | Disparity analysis paper |
| Length | About 1,340 words, 4 pages |
| Format | APA 7 student paper |
| School | Chamberlain University |
| Program | RN-to-BSN |
| Updated | October 2026 |
Free sample paper for NR 307A Week 3
"He's Just Here for the Pills": Testing a Belief About Pain Medication Against National Emergency Department Data
Student Name
College of Nursing, Chamberlain University
NR 307A: Advancing Equity, Inclusion, and Diversity in Healthcare
Instructor Name
Month Day, Year
"He's Just Here for the Pills": Testing a Belief About Pain Medication Against National Emergency Department Data
The Claim
On the emergency department where I float two shifts a month, one sentence comes up more often than any other when a patient asks for more pain medicine: "He's just here for the pills." I have heard it said about patients of every background, but when I tried to recall the last ten times, most of them were about Black or Hispanic men in their twenties to forties with back pain or abdominal pain. I do not have a count of those ten times, only my memory, and memory is the kind of evidence this assignment is meant to replace.
Stated as a testable claim, the belief goes something like this: Black and Hispanic patients who come to the emergency department with pain are more likely than white patients to be seeking opioids they do not need, and so they receive, or should receive, opioids less readily. If the first half were true, we would expect to see it in national data as a pattern of higher opioid use or prescribing among these groups. If it is not true, the data should show something else.
What Would Settle It
Before searching, I decided that three kinds of figures would speak to the claim. First, national rates of opioid prescribing at emergency department visits for pain, by race and ethnicity. Second, whether any difference holds for conditions where pain is objectively confirmed, such as a broken bone or a kidney stone, compared with conditions where the clinician depends on the patient's report, such as back pain. Third, a pooled estimate across studies, so that one survey does not decide the question. I limited the sources to national survey data and peer-reviewed studies, since I cannot verify anything from my employer's internal system in a classroom paper.
What the Figures Show
Pletcher et al. (2008) analyzed 13 years of the National Hospital Ambulatory Medical Care Survey, covering pain-related emergency department visits from 1993 to 2005. Across all years, white patients with pain received an opioid at 31% of visits, compared with 23% for Black patients and 24% for Hispanic patients. The gap did not narrow over time; in 2005, the figures were 40% for white patients and 32% for all other patients. The difference was larger for more severe pain and appeared even for long-bone fractures and kidney stones.
Singhal et al. (2016) used the same national survey for 2007 to 2011, focusing on adults aged 18 to 65, and asked the question behind the claim more directly. They compared conditions often associated with drug-seeking, which they listed as toothache, back pain and abdominal pain, with conditions where pain is objectively confirmed, long-bone fractures and kidney stones. After adjustment, Black patients were less likely than white patients to be prescribed an opioid at discharge for back pain and abdominal pain, with adjusted odds ratios between 0.56 and 0.67. They found no significant difference for toothache, fractures or kidney stones.
Lee et al. (2019) pooled 14 studies of acute pain in US emergency departments. Black patients were less likely than white patients to receive any pain medicine for acute pain, with an odds ratio of 0.60. For Hispanic patients the odds ratio was 0.75, but the confidence interval included no difference, so that estimate is less certain.
Table 1
Pain Medication in US Emergency Departments by Race and Ethnicity
| Source and population | Measure | White | Black | Hispanic |
|---|---|---|---|---|
| Pletcher et al. (2008); national ED pain visits, 1993-2005 | Opioid given or prescribed | 31% | 23% | 24% |
| Singhal et al. (2016); national ED visits, adults 18-65, 2007-2011 | Opioid prescribed at discharge, back or abdominal pain (adjusted odds ratio vs. white) | 1.00 (reference) | 0.56 to 0.67 | Not significant |
| Lee et al. (2019); 14 US ED studies, 1990-2018 | Any analgesia for acute pain (odds ratio vs. white) | 1.00 (reference) | 0.60 | 0.75 (interval includes no difference) |
Note. ED = emergency department. Figures are taken from the published abstracts and reports of each study. Odds ratios below 1.00 mean the group was less likely than white patients to receive the medication.
The Part That Did Not Match
Not every figure pointed the same way. Guedj et al. (2021) studied 8,347 children with limb fractures at one pediatric emergency department from 2011 to 2015. Black and Hispanic children were less likely than white children to receive an opioid, with adjusted odds ratios of 0.61 and 0.66, but they were more likely to receive some pain medicine of any kind, with adjusted odds ratios of 1.63 and 1.43. In that setting, the difference was not whether pain was treated but how. This complicates a simple story in which minority patients are always undertreated, and it points toward the choice of drug as the place where decisions differ. It does not support the claim either: nothing in it suggests that minority children were seeking medication they did not need.
What the Figures Say About the Claim
The claim assumed that Black and Hispanic patients are more likely to be seeking opioids. The national data do not measure motives, so they cannot prove or disprove what any one patient intends. What they do show is the opposite of what the claim would predict in practice: across two decades of national survey data and a pooled analysis, Black patients, and to a lesser and less certain degree Hispanic patients, received opioids or any pain medicine less often than white patients with similar complaints. Most tellingly, Singhal et al. (2016) found the gap specifically in the conditions where clinicians rely on the patient's report, and not where pain is confirmed by an X-ray or a scan. That is the pattern one would expect if clinicians' judgments about who is believable, rather than patients' behavior, were driving the difference.
Hoffman et al. (2016) offer one explanation for how such judgments form. In a study of white medical students and residents, about half endorsed at least one false belief about biological differences between Black and white people, such as thicker skin, and those who did rated a Black patient's pain as lower and made less accurate treatment recommendations. The claim I started with, "he's just here for the pills," is a judgment about believability made at the bedside, and the figures suggest it is applied unevenly.
From the Patients to the Arrangements
The most important turn in this analysis is that the figures describe what was done to patients, not what the patients are. A lower rate of opioid prescribing for Black patients with back pain is a fact about prescribing, and prescribing is shaped by arrangements: whether pain is reassessed with the same scale for everyone, whether a clinician's note must record the reason when a requested analgesic is withheld, and whether a unit ever reviews its own prescribing by race and ethnicity. None of those arrangements currently exists in a formal way on the unit where I float.
One small change follows from this. The emergency department already requires a pain score at triage and after each intervention. Adding a required field to the pain reassessment, "If requested medication is not given, reason," would give the department a record that could later be reviewed by patient group. It would not prove anyone's intent, but it would make the reasons visible and comparable, which is what the figures above were built from.
Limitations
The national survey data are older than I would like, and the pooled analysis relies on studies of varying quality. Race and ethnicity in these data are recorded by hospital staff, not always by the patients themselves. None of these sources describes my own unit. They can tell me what the claim would have to overcome, not what happens on any particular shift.
References
Guedj, R., Marini, M., Kossowsky, J., Berde, C. B., Kimia, A. A., & Fleegler, E. W. (2021). Racial and ethnic disparities in pain management of children with limb fractures or suspected appendicitis: A retrospective cross-sectional study. Frontiers in Pediatrics, 9, Article 652854. https://doi.org/10.3389/fped.2021.652854
Hoffman, K. M., Trawalter, S., Axt, J. R., & Oliver, M. N. (2016). Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. Proceedings of the National Academy of Sciences, 113(16), 4296-4301. https://doi.org/10.1073/pnas.1516047113
Lee, P., Le Saux, M., Siegel, R., Goyal, M., Chen, C., Ma, Y., & Meltzer, A. C. (2019). Racial and ethnic disparities in the management of acute pain in US emergency departments: Meta-analysis and systematic review. American Journal of Emergency Medicine, 37(9), 1770-1777. https://doi.org/10.1016/j.ajem.2019.06.014
Pletcher, M. J., Kertesz, S. G., Kohn, M. A., & Gonzales, R. (2008). Trends in opioid prescribing by race/ethnicity for patients seeking care in US emergency departments. JAMA, 299(1), 70-78. https://doi.org/10.1001/jama.2007.64
Singhal, A., Tien, Y.-Y., & Hsia, R. Y. (2016). Racial-ethnic disparities in opioid prescriptions at emergency department visits for conditions commonly associated with prescription drug abuse. PLOS ONE, 11(8), Article e0159224. https://doi.org/10.1371/journal.pone.0159224
Reading the NR 307A Week 3 instructions
In Week 3 you take one health disparity and examine it using national, state or peer-reviewed data. Expect the instructions to ask for a clear question or claim, credible data sources with years, an analysis of what the figures show, consideration of contradicting evidence and implications for practice, in APA, often with a table. Write the claim the way it is actually said, then restate it so data could confirm or contradict it. Decide what figures would settle it before you search. Report each figure with its group and year. Give contradicting findings their own space. Describe what the figures say about arrangements, not about the patients. Discuss any table you include.
How the NR 307A Week 3 example is put together
The paper opens with the sentence heard on the emergency floor and restates it as a testable claim about opioid seeking. A section sets three kinds of evidence in advance. The findings follow: Pletcher et al. (2008) with 13 years of national survey data, Singhal et al. (2016) comparing conditions associated with drug seeking and Lee et al. (2019) pooling 14 studies with an odds ratio of 0.60 for Black patients. Table 1 sets the figures side by side with a note on odds ratios. Guedj et al. (2021) is reported as the part that did not match. Hoffman et al. (2016) explains how false beliefs shape judgment. A small documentation change and limitations close the paper.
Where the points sit in the NR 307A Week 3 rubric
Disparity analysis rubrics typically reward a clearly stated question, credible and current data, accurate interpretation, treatment of contradicting evidence, a focus on systems rather than individuals, implications and APA including tables. Interpretation carries the most weight, so each figure is reported with its group, measure and years, and the paper states what the data can and cannot show about motives. Contradicting evidence earns credit in its own section. The systems focus is scored when the analysis turns from patients to prescribing arrangements. Implications are graded on feasibility, such as one added field. A table with a title and note in APA format protects points that are often lost.
Common NR 307A Week 3 mistakes, and how to avoid them
The most expensive error treats the count as a description of patients rather than of what was arranged around them. Next is never stating the claim being tested, leaving figures that answer no question. Sources that are not national, state or peer reviewed, figures with no year, a table never discussed, percentages without their group and a conclusion that recommends training for a structural finding all cost points. APA errors in table titles and notes are marked often. Use your employer's numbers only with permission and without identifying the organization. If the numbers contradict the claim, say so; that is the finding. Send the claim you want to test, and the analysis can be built around national data.
Get a NR 307A Week 3 example written to your instructions
Send the Week 3 instructions, the rubric and any list of approved data sources your classroom names, and a custom example is written to that material and returned inside 24 to 48 hours, free the first time. If your section requires figures from a particular reporting system, say which one and the example will be built on it.
More NR 307A papers and RN-to-BSN samples
- NR 307A Week 1: Equal Versus Equitable Post
- NR 307A Week 2: Implicit Bias Self-Audit Reflection
- NR 307A Week 4: Organizational Assessment
- NR 307A Week 5: Access Barrier Brief
- NR 307A Week 6: Inclusive Communication Plan
- NR 307A Week 7: Policy Proposal
- NR 307A Week 8: Reflection
- NR 351 Week 3: Professional Identity Paper
- NR 306 Week 3: Head Neck and Sinus Exam Write-Up
- NR 360 Week 3: Workflow Analysis
- NR 305 Week 3: Focused Family History Write-Up
NR 307A Week 3 questions, answered
Can I use my employer's own numbers?
Usually not, and sections that allow it ask for permission in writing first. Internal reports are not something a marker can check, and pulling them can breach an employer policy. Published national and state reporting is accepted everywhere and is easier to defend. If you have a workplace figure you trust, mention it as context and rest the argument on the public source.
What if the numbers contradict the claim?
That is a stronger paper, not a failed one. An analysis that set out its evidence in advance and then reported a result it did not expect has demonstrated exactly the discipline the rubric is looking for. Write the contradiction plainly, say what the claim probably rests on instead, and resist rescuing the original belief in the conclusion.
How much statistics does this need?
Very little. Sections commonly want figures compared and explained rather than tested, so a rate set beside another rate with both years stated is enough. Anything more advanced usually belongs to a research course. What the marker checks is that every figure is sourced, that the groups being compared are named, and that the prose says what the comparison means.
Where can I find a free NR 307A Week 3 sample paper?
The complete Week 3 disparity analysis testing the belief about pain medication against national emergency department data is printed further down this page.
What do the figures in the NR 307A Week 3 analysis show?
Black and Hispanic patients with pain were less likely than white patients to receive opioids, including for back and abdominal pain, which is the opposite of what the drug-seeking claim would predict.