HCD 400 Module 5 Module 5 Assignment: Implicit Biases, Assumptions and Limitations Example

Reviewed by Emmett Rockwell, MBA Arizona State University Updated October 2026

This HCD 400 Module 5 sample responds to the third essay of Evidence Odyssey, the research appraisal course in ASU's Health Care Administration and Policy bachelor's program. Carrying 60 points, this ASU HCD 400 essay calls on students to appraise the implicit biases, assumptions and limitations in health research articles and to explain how they shape the way findings should be read. The composite student returns to the community health worker trial and the systematic review she has followed since Module 2. She examines the assumptions in eligibility, outcomes, program delivery and setting, the biases in reviews that depend on what gets published and how each could tilt a hospital administrator's decision.

CourseHCD 400 Evidence Odyssey Decoding Peer-Reviewed Research for Future Practice
ModuleModule 5
Paper typeResearch appraisal essay
LengthAbout 582 words, 5 pages
FormatAPA 7 student paper
SchoolArizona State University
ProgramBS in Health Care Administration and Policy
UpdatedOctober 2026

Free sample paper for HCD 400 Module 5

1

What the Studies Took for Granted: Assumptions, Biases and Limits in Research on Community Health Workers

Student Name

BS in Health Care Administration and Policy, Arizona State University

HCD 400: Evidence Odyssey: Decoding Peer-Reviewed Research for Future Practice

Instructor Name

Month Day, Year

What this page is doingThe title frames the essay around unstated assumptions, which are harder to spot than listed limitations and are the focus of this module.
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What the Studies Took for Granted: Assumptions, Biases and Limits in Research on Community Health Workers

Introduction

Every study makes choices that its authors may not name as limitations: who counts as eligible, what counts as success, where the study happens and who delivers the intervention. These choices carry assumptions, and assumptions can bias how findings are interpreted. This essay examines those choices in two sources: a randomized trial of a community health worker program (Kangovi et al., 2018) and a systematic review of community health workers and health care use (Jack et al., 2017).

Assumptions About Who Benefits

The trial enrolled adults from high-poverty ZIP codes who had public coverage or none and carried multiple chronic diagnoses. The assumption is that this group has the most to gain from help with social needs. That is reasonable, but it means the results say little about patients with one condition or private insurance. An administrator in a suburban system should not assume the same effect.

Assumptions About What Counts as Success

The trial's primary outcome was self-rated physical health. Choosing a patient-reported outcome reflects a value that patients' own judgments matter, which many would endorse. Yet the program's clearest benefits appeared in hospital use and ratings of care, not in the primary outcome. A reader who focuses only on hospital days may assume the program made patients healthier, when the trial did not show that.

What this page is doingThe essay separates a value judgment built into the study, choosing a patient-reported primary outcome, from a bias in how readers might interpret it, which is the kind of distinction this module asks for.
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Assumptions About Delivery and Setting

The program was delivered by community health workers who were hired, trained and supervised through a dedicated academic center, across three primary care facilities in one city. The assumption is that other organizations can reproduce this support. Many cannot. Results from an experienced, well-resourced program may overstate what a new program would achieve, a common problem when interventions move from research to practice.

Biases in Reviews

Systematic reviews inherit the biases of the studies they include. The review I followed found that randomized trials were less likely than weaker designs to show reductions in health care use (Jack et al., 2017). This pattern suggests that weaker designs may overstate benefits, and that studies with positive results may be more likely to be published. Reviews limited to English-language, published studies may also miss null findings. A second review, which admitted only English-language randomized trials published before August 2014 (Kim et al., 2016), shows the trade-off: such limits make a review rigorous but leave out unpublished and non-English work. A reader who counts positive studies without weighing design would be misled.

The Reader's Own Bias

Finally, I had to examine my own bias. I began this course hoping community health workers would be the answer for my future employer. That hope made it easy to give the hospital-days result more weight than the null primary outcome.

How These Elements Change Interpretation

ElementPossible effect on interpretationHow to read the findings
Narrow eligibilityOverstates benefits for other populationsApply to similar patients
Null primary outcomeReaders may assume health improvedReport both primary and secondary results
Well-resourced settingOverstates effects in new programsPlan for smaller effects and evaluation
Publication and design patternsOverstates benefits in reviewsWeight trials more heavily

Conclusion

Neither source is flawed in a way that makes it untrustworthy. Their assumptions are reasonable but limit how far the findings travel. The fair conclusion is that well-run community health worker programs can reduce hospital use for high-need, low-income patients, and that a new program should be evaluated rather than assumed to succeed.

References

Jack, H. E., Arabadjis, S. D., Sun, L., Sullivan, E. E., & Phillips, R. S. (2017). Impact of community health workers on use of healthcare services in the United States: A systematic review. Journal of General Internal Medicine, 32(3), 325-344. https://doi.org/10.1007/s11606-016-3922-9

Kangovi, S., Mitra, N., Norton, L., Harte, R., Zhao, X., Carter, T., Grande, D., & Long, J. A. (2018). Effect of community health worker support on clinical outcomes of low-income patients across primary care facilities: A randomized clinical trial. JAMA Internal Medicine, 178(12), 1635-1643. https://doi.org/10.1001/jamainternmed.2018.4630

Kim, K., Choi, J. S., Choi, E., Nieman, C. L., Joo, J. H., Lin, F. R., Gitlin, L. N., & Han, H. R. (2016). Effects of community-based health worker interventions to improve chronic disease management and care among vulnerable populations: A systematic review. American Journal of Public Health, 106(4), e3-e28. https://doi.org/10.2105/AJPH.2015.302987

HCD 400 Module 5 instructions, in plain terms

Worth 60 points, the Module 5 Assignment is the third of HCD 400's four essays. The syllabus asks students to critically appraise implicit biases, assumptions and limitations within research articles related to health fields and to understand how these elements influence the interpretation of findings. Listed limitations, the ones authors write in their discussion section, are only a starting point; the module is about the choices authors do not label as limitations, such as who was studied and what counted as success. Using the sources from your earlier essays lets you build a deeper appraisal rather than starting over. The five percent limit on quotation applies here too.

How this HCD 400 Module 5 example is built

Organized by kind of assumption, the sample takes eligibility, the choice of outcome, the setting and delivery of the program and the biases that reviews inherit, each with a short explanation of how it could tilt the reader's conclusion. A section on the reader's own bias shows the self-awareness the topic invites. A table pairs each element with its possible effect and the fair way to read the findings, and the conclusion states the claim the evidence supports. Two sources from the earlier essays anchor the analysis, and a third is cited for context. Each assumption is tied to a concrete decision an administrator might make, which keeps the appraisal practical rather than abstract and shows why the module matters for health care management.

Reading the HCD 400 Module 5 grading rubric

The Module 5 essay earns up to 60 points on the Canvas rubric. Appraisals of bias and assumptions are typically credited for identifying assumptions that are not stated as limitations, explaining specifically how each could affect interpretation, distinguishing study-level problems from reader-level bias, accurate understanding of the studies and a balanced conclusion about what the evidence supports. Essays lose credit when they simply repeat the authors' limitations section, when they call a study biased without explaining the mechanism, when they dismiss studies for ordinary limitations and when they quote heavily. Rubrics in appraisal courses also tend to reward essays that name the direction of a bias, toward overstating or understating an effect, rather than simply noting that bias exists.

HCD 400 Module 5 help: mistakes that cost marks

Students often copy the limitations paragraph from the article. Look instead at the choices behind the study: who was included, what was measured, where it happened, who delivered the intervention and what a review could not see. For each, explain the direction it could push the results. Admit your own expectations. Avoid declaring a study worthless; most limitations narrow conclusions rather than erase them. Paraphrase and stay under the five percent quotation limit. The desk can help you list the assumptions in your article if you share it. One quick test: for every assumption you name, finish the sentence 'so a reader should' with a specific adjustment to how the findings are used.

Write yours, or have the desk draft it

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HCD 400 Module 5 questions, answered

Where can I find a free HCD 400 Module 5 sample paper?

This page carries a full HCD 400 Module 5 sample: an essay on the assumptions, biases and limits in community health worker research.

What is the difference between a limitation and an assumption in research?

Limitations are weaknesses authors acknowledge; assumptions are choices built into the study, often unstated, such as who was eligible or what counted as success.

What is publication bias?

When null or negative studies stay unpublished more often than favorable ones, reviews of the published record can overstate benefits.

How do implicit biases affect research interpretation?

They can lead readers to give more weight to findings that fit their expectations, such as favoring a positive secondary outcome over a null primary one.

How much is the HCD 400 Module 5 assignment worth?

It is worth 60 points.