1 MAXE • 152 SRUN
★ ★
Chamberlain University
C College of Nursing & Public Health
J O U R N E Y T O E X T R A O R D I N A R Y CO M PA S S I O N AT E C A R E
EST. 1889
NURS 251 — Examination 1
H E A LT H A SS E SS M E N T, C L I N I C A L R E A S O N I N G & D I A G N O ST I C B I A S
INSTITUTION Chamberlain University — College of COURSE CODE NURS 251
Nursing
PROGRAM Bachelor of Science in Nursing (BSN) ACADEMIC YEAR
EXAM TITLE Examination 1 — Health Assessment TOTAL QUESTIONS 76 Questions
Foundations
ACCREDITATION CCNE — Commission on Collegiate FORMAT Multiple Choice — Select the Single Best
Nursing Education Answer
EXAMINATION INSTRUCTIONS
▸ Select the single best answer for each question based on NURS 251 course content.
▸ Questions cover diagnostic bias, sensitivity/specificity, nursing diagnosis structure, mental status assessment, motivational
interviewing, and clinical reasoning.
▸ Understanding the OLD CART and FIFE mnemonics, MMSE scoring, and levels of consciousness is essential.
▸ Correct answers and clinical rationales appear below each question for examination review.
▸ All content aligns with Chamberlain University BSN curriculum and CCNE accreditation standards.
SECTION I — DIAGNOSTIC BIAS, TEST CHARACTERISTICS, NURSING Questions 1
DIAGNOSIS & ASSESSMENT – 76
1. What is confirmation bias in the context of clinical reasoning?
A. Seeking out rare diagnoses because they are more interesting
B. Overlooking things that do not confirm the preconception — only looking at symptoms that confirm the original
diagnosis while ignoring signs that may suggest an alternative
C. Assuming a common diagnosis based on the patient's age and gender
D. Changing the diagnosis every time new data is presented
CORRECT ANSWER B — Overlooking things that do not confirm the preconception; only looking at symptoms that confirm
the original diagnosis
RATIONALE Confirmation bias is one of the most common and dangerous cognitive errors in clinical practice. The clinician
forms an early diagnostic impression and then selectively attends to data that supports that diagnosis while
ignoring or discounting evidence that contradicts it. This can lead to missed diagnoses, delayed treatment,
and patient harm. The antidote is actively seeking disconfirming evidence — deliberately asking "What
findings would suggest this is NOT the correct diagnosis?"
,2. How is sensitivity defined in diagnostic testing?
A. How accurate a test is at identifying people who do not have a disease
B. How expensive a test is compared to its alternatives
C. How accurate a test is at identifying people who actually have a disease (true positive)
D. How quickly a test returns results
CORRECT ANSWER C — How accurate a test is at identifying people who actually have a disease (true positive)
RATIONALE Sensitivity answers the question: "If the patient has the disease, how likely is the test to be positive?" A highly
sensitive test (approaching 100%) has a very low false-negative rate — it misses few cases. The mnemonic is
SNNOUT: a Sensitive test with a Negative result helps rule OUT the disease. High sensitivity is critical when
missing a diagnosis would have severe consequences (e.g., HIV screening, meningitis). However, highly
sensitive tests may produce false positives.
3. How is specificity defined in diagnostic testing?
A. How accurate a test is at identifying people who actually have a disease
B. How accurate a test is at identifying people who do not have a disease (true negative)
C. How specific the test is to one organ system
D. How many different diseases the test can detect
CORRECT ANSWER B — How accurate a test is at identifying people who do not have a disease (true negative)
RATIONALE Specificity answers the question: "If the patient does NOT have the disease, how likely is the test to be
negative?" A highly specific test (approaching 100%) has a very low false-positive rate. The mnemonic is
SPPIN: a Specific test with a Positive result helps rule IN the disease. High specificity is critical when a false-
positive result would lead to harmful interventions (e.g., cancer biopsy, major surgery). However, highly
specific tests may miss some cases (false negatives).
4. In a population of 10 women where 3 have cancer and 7 are cancer-free, a test correctly identifies all 3 women with
cancer and all 7 without cancer. How are its specificity and sensitivity described?
A. Low specificity and low sensitivity
B. Low specificity and high sensitivity
C. High specificity and high sensitivity
D. High specificity and low sensitivity
CORRECT ANSWER C — High specificity and high sensitivity
RATIONALE This is the ideal test — a perfect diagnostic tool. Sensitivity = true positives / (true positives + false negatives) =
3/(3+0) = 100%. Specificity = true negatives / (true negatives + false positives) = 7/(7+0) = 100%. The test
correctly identifies every person with the disease (no false negatives → high sensitivity) and correctly
identifies every person without the disease (no false positives → high specificity). In clinical practice, no test
achieves 100% on both measures simultaneously; there is typically a trade-off between sensitivity and
specificity.
, 5. In a population of 10 women where 3 have cancer and 7 are cancer-free, a test correctly identifies the 3 with cancer
but also incorrectly identifies 3 cancer-free women as having cancer. How are its specificity and sensitivity
described?
A. High specificity and high sensitivity
B. Low specificity and high sensitivity
C. Low specificity and low sensitivity
D. High specificity and low sensitivity
CORRECT ANSWER B — Low specificity and high sensitivity
RATIONALE Sensitivity = 3/(3+0) = 100% (high — the test caught all true cases, zero false negatives). Specificity = 4/(4+3) =
57% (low — the test incorrectly flagged 3 healthy women as diseased, producing false positives). This pattern
describes a highly sensitive but poorly specific test — it "casts a wide net" and catches all true cases but at the
cost of many false alarms. This is acceptable when the consequences of missing a case are severe (the false
positives can be resolved with confirmatory testing), but problematic if false positives lead to harmful
interventions.
6. In a population of 10 women where 3 have cancer and 7 are cancer-free, a test correctly identifies only 1 woman
with cancer and also incorrectly identifies 1 cancer-free woman as having cancer. How are its specificity and
sensitivity described?
A. High specificity and high sensitivity
B. Low specificity and high sensitivity
C. Low specificity and low sensitivity
D. High specificity and low sensitivity
CORRECT ANSWER C — Low specificity and low sensitivity
RATIONALE Sensitivity = 1/(1+2) = 33% (low — the test missed 2 of 3 cancer cases). Specificity = 6/(6+1) = 86% (technically
moderate-high, but in the framework of the question, the specificity is described as low because the test
produced a false positive while also failing to detect most true cases). This represents a poor diagnostic test
that both misses true disease AND produces false alarms — the worst combination. Such a test has minimal
clinical utility and should not be used for screening or diagnosis.
7. When is high sensitivity particularly important in clinical testing?
A. When confirming a diagnosis before major surgery
B. When it is important not to miss a problem — this may cause false positives
C. When the disease is extremely rare in the population
D. When the test is expensive and must justify its cost
CORRECT ANSWER B — When it is important not to miss a problem — this may cause false positives
RATIONALE High sensitivity is prioritized when the consequence of a missed diagnosis (false negative) is catastrophic.
Classic examples include: HIV screening (missing a case means ongoing transmission), bacterial meningitis
(missing a case means death), newborn metabolic screening (missing PKU means irreversible brain damage),
and suicidal ideation screening (missing a case means potential suicide). The trade-off — accepting some
false positives — is manageable because false positives can be resolved with more specific confirmatory
testing (e.g., Western blot after positive HIV ELISA).
★ ★
Chamberlain University
C College of Nursing & Public Health
J O U R N E Y T O E X T R A O R D I N A R Y CO M PA S S I O N AT E C A R E
EST. 1889
NURS 251 — Examination 1
H E A LT H A SS E SS M E N T, C L I N I C A L R E A S O N I N G & D I A G N O ST I C B I A S
INSTITUTION Chamberlain University — College of COURSE CODE NURS 251
Nursing
PROGRAM Bachelor of Science in Nursing (BSN) ACADEMIC YEAR
EXAM TITLE Examination 1 — Health Assessment TOTAL QUESTIONS 76 Questions
Foundations
ACCREDITATION CCNE — Commission on Collegiate FORMAT Multiple Choice — Select the Single Best
Nursing Education Answer
EXAMINATION INSTRUCTIONS
▸ Select the single best answer for each question based on NURS 251 course content.
▸ Questions cover diagnostic bias, sensitivity/specificity, nursing diagnosis structure, mental status assessment, motivational
interviewing, and clinical reasoning.
▸ Understanding the OLD CART and FIFE mnemonics, MMSE scoring, and levels of consciousness is essential.
▸ Correct answers and clinical rationales appear below each question for examination review.
▸ All content aligns with Chamberlain University BSN curriculum and CCNE accreditation standards.
SECTION I — DIAGNOSTIC BIAS, TEST CHARACTERISTICS, NURSING Questions 1
DIAGNOSIS & ASSESSMENT – 76
1. What is confirmation bias in the context of clinical reasoning?
A. Seeking out rare diagnoses because they are more interesting
B. Overlooking things that do not confirm the preconception — only looking at symptoms that confirm the original
diagnosis while ignoring signs that may suggest an alternative
C. Assuming a common diagnosis based on the patient's age and gender
D. Changing the diagnosis every time new data is presented
CORRECT ANSWER B — Overlooking things that do not confirm the preconception; only looking at symptoms that confirm
the original diagnosis
RATIONALE Confirmation bias is one of the most common and dangerous cognitive errors in clinical practice. The clinician
forms an early diagnostic impression and then selectively attends to data that supports that diagnosis while
ignoring or discounting evidence that contradicts it. This can lead to missed diagnoses, delayed treatment,
and patient harm. The antidote is actively seeking disconfirming evidence — deliberately asking "What
findings would suggest this is NOT the correct diagnosis?"
,2. How is sensitivity defined in diagnostic testing?
A. How accurate a test is at identifying people who do not have a disease
B. How expensive a test is compared to its alternatives
C. How accurate a test is at identifying people who actually have a disease (true positive)
D. How quickly a test returns results
CORRECT ANSWER C — How accurate a test is at identifying people who actually have a disease (true positive)
RATIONALE Sensitivity answers the question: "If the patient has the disease, how likely is the test to be positive?" A highly
sensitive test (approaching 100%) has a very low false-negative rate — it misses few cases. The mnemonic is
SNNOUT: a Sensitive test with a Negative result helps rule OUT the disease. High sensitivity is critical when
missing a diagnosis would have severe consequences (e.g., HIV screening, meningitis). However, highly
sensitive tests may produce false positives.
3. How is specificity defined in diagnostic testing?
A. How accurate a test is at identifying people who actually have a disease
B. How accurate a test is at identifying people who do not have a disease (true negative)
C. How specific the test is to one organ system
D. How many different diseases the test can detect
CORRECT ANSWER B — How accurate a test is at identifying people who do not have a disease (true negative)
RATIONALE Specificity answers the question: "If the patient does NOT have the disease, how likely is the test to be
negative?" A highly specific test (approaching 100%) has a very low false-positive rate. The mnemonic is
SPPIN: a Specific test with a Positive result helps rule IN the disease. High specificity is critical when a false-
positive result would lead to harmful interventions (e.g., cancer biopsy, major surgery). However, highly
specific tests may miss some cases (false negatives).
4. In a population of 10 women where 3 have cancer and 7 are cancer-free, a test correctly identifies all 3 women with
cancer and all 7 without cancer. How are its specificity and sensitivity described?
A. Low specificity and low sensitivity
B. Low specificity and high sensitivity
C. High specificity and high sensitivity
D. High specificity and low sensitivity
CORRECT ANSWER C — High specificity and high sensitivity
RATIONALE This is the ideal test — a perfect diagnostic tool. Sensitivity = true positives / (true positives + false negatives) =
3/(3+0) = 100%. Specificity = true negatives / (true negatives + false positives) = 7/(7+0) = 100%. The test
correctly identifies every person with the disease (no false negatives → high sensitivity) and correctly
identifies every person without the disease (no false positives → high specificity). In clinical practice, no test
achieves 100% on both measures simultaneously; there is typically a trade-off between sensitivity and
specificity.
, 5. In a population of 10 women where 3 have cancer and 7 are cancer-free, a test correctly identifies the 3 with cancer
but also incorrectly identifies 3 cancer-free women as having cancer. How are its specificity and sensitivity
described?
A. High specificity and high sensitivity
B. Low specificity and high sensitivity
C. Low specificity and low sensitivity
D. High specificity and low sensitivity
CORRECT ANSWER B — Low specificity and high sensitivity
RATIONALE Sensitivity = 3/(3+0) = 100% (high — the test caught all true cases, zero false negatives). Specificity = 4/(4+3) =
57% (low — the test incorrectly flagged 3 healthy women as diseased, producing false positives). This pattern
describes a highly sensitive but poorly specific test — it "casts a wide net" and catches all true cases but at the
cost of many false alarms. This is acceptable when the consequences of missing a case are severe (the false
positives can be resolved with confirmatory testing), but problematic if false positives lead to harmful
interventions.
6. In a population of 10 women where 3 have cancer and 7 are cancer-free, a test correctly identifies only 1 woman
with cancer and also incorrectly identifies 1 cancer-free woman as having cancer. How are its specificity and
sensitivity described?
A. High specificity and high sensitivity
B. Low specificity and high sensitivity
C. Low specificity and low sensitivity
D. High specificity and low sensitivity
CORRECT ANSWER C — Low specificity and low sensitivity
RATIONALE Sensitivity = 1/(1+2) = 33% (low — the test missed 2 of 3 cancer cases). Specificity = 6/(6+1) = 86% (technically
moderate-high, but in the framework of the question, the specificity is described as low because the test
produced a false positive while also failing to detect most true cases). This represents a poor diagnostic test
that both misses true disease AND produces false alarms — the worst combination. Such a test has minimal
clinical utility and should not be used for screening or diagnosis.
7. When is high sensitivity particularly important in clinical testing?
A. When confirming a diagnosis before major surgery
B. When it is important not to miss a problem — this may cause false positives
C. When the disease is extremely rare in the population
D. When the test is expensive and must justify its cost
CORRECT ANSWER B — When it is important not to miss a problem — this may cause false positives
RATIONALE High sensitivity is prioritized when the consequence of a missed diagnosis (false negative) is catastrophic.
Classic examples include: HIV screening (missing a case means ongoing transmission), bacterial meningitis
(missing a case means death), newborn metabolic screening (missing PKU means irreversible brain damage),
and suicidal ideation screening (missing a case means potential suicide). The trade-off — accepting some
false positives — is manageable because false positives can be resolved with more specific confirmatory
testing (e.g., Western blot after positive HIV ELISA).
