Initials – D.G DOB 10/17/2017 Age 7
brought to the hospital with inpetigo and fever, skin lesions/ warm and dry blisters on hands (bumps with pus) – pending labs,blood culture
Contact precautions for staphylococcus
no allergies
history – Coartraction of aorta, horsesechoe kidney, asthma, trach that was removed 4/2025
IV LFA #22G
Meds – Zosyn Q6
Zyvox Q12
D5LR @65 ML/HR
Aceteminophen 160mg/5ml oral
clonidine 0.1mg tab
pepcid 40mg/5ml oral
Clinical Judgement Plan
Instructor:
DATE Care Provided and UNIT:
Student Name
Clinical Judgement Plan
West Coast University
Professor Name
Date
Social History
Patient Information
Patient Initials:
Admission Date:
Chief Complaint:
Age & Gender:
Weight:
BMI:
Allergies:
Code Status:
Living Will/ DPOA:
History of Present Illness (HPI)
Admitting Diagnosis & Pathophysiology
Medical History & Pathophysiology
Surgical History & Pathophysiology
Erikson’s Developmental Stage Related to Patient (1) *List and discuss specific stage (based on objective assessment)
Social Determinants of Health
Ethnicity
Occupation
Religion
Family support
Insurance
3 Psychosocial Considerations/Concerns
Teaching Assessment and Client Education
Interprofessional Consults and Multidisciplinary Plan
Discharge Planning
Lab Tests with Values
(Include normal ranges, dates, and rationales of abnormal results)
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Lab Tests or
Diagnostic Tests
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Normal Ranges
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Admission Lab Values
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Current Lab Values
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Explain Abnormal Results R/T Your Patient
(USE additional pages at the end of template WHEN NEEDED)
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Diagnostics
(3) Relevant Diagnostic Procedures with Results
(2) Medications
Medication Name
Include Generic name, Trade name, and Medication Class.
Include OTC, herbal (non-pharmacological items) and PRN medications given during clinical
Dose
Must include dosage calculation for min and max per weight.
Route
Frequency
Purpose of Medication for Your Patient
Mechanism of Action
Side Effects/
Adverse Reactions
Nursing Considerations Specific to Your Patient
Physical Assessment/Review of Systems
HEENT
Hormone Regulation/Reproduction/
Endocrine (13)
IV Lines/Drains/Tubes
Psychosocial (14)
Vital Signs/Height/Weight (4)
Temp:
HR:
BP:
RR:
SpO2:
Pain:
Height:
Weight:
Respiratory (7)
Cardiovascular (6)
Neurological (5)
Genitourinary (GU) (10)
Musculoskeletal and Activity (11)
Hydration/Nutrition (8) and Gastrointestinal (GI) (9)
Integumentary (12)
Responding
Observation
Interpreting
Implement
Planning
Analysis
Assessment
Take Action
Generate Solutions
Prioritize Hypotheses
Analyze Cues
Recognize Cues
Evaluate
Evaluation
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Reference Page
Respond to any questions you may have been asked by your peers in your assigned group area . Note what you have learned and/or any insights you have gained as a result of reading the comments your peers made.
Respond to any questions you may have been asked by your peers in your assigned group area . Note what you have learned and/or any insights you have gained as a result of reading the comments your peers made.
AE
Afia Ewoo
Sep 8 6:38pm| Last reply Sep 9 5:31pm
Reply from Afia Ewoo
Analysis of Observational Study Designs in Epidemiology
Whittle and Diaz-Artiles (2020) conducted ecological research analyzing socioeconomic factors affecting COVID-19 positivity prevalence in New York City. This study demonstrates ecological design by utilizing population-level data to examine broad trends, such as the impact of socioeconomic factors on health outcomes. A significant strength of this approach is its ability to reveal associations between socioeconomic variables and health outcomes across large populations. However, making assumptions about individual behavior based on group data can lead to ecological fallacy, a key limitation. Despite this limitation, the study effectively utilizes neighborhood-level data to address public health concerns, highlighting the intersection of socioeconomic determinants and health throughout the pandemic. The result prompts a discussion over the appropriateness of ecological design for the research topics posed.
This study investigates health-related quality of life in chronically homeless individuals across various supportive housing types, citing the cross-sectional research conducted by Spector et al. (2020). Cross-sectional studies are ideal for assessing prevalence and associations at a particular time, as they provide a snapshot of a population's health status. The study's strength is its ability to gather data on demographics and health behaviors rapidly. This investigation provides valuable insights into the efficacy of permanent supportive housing models.
The limitation is its inability to determine causation, indicating that the observed correlations may not represent authentic cause-and-effect relationships. The study demonstrates the effectiveness of cross-sectional designs in informing health interventions by providing actionable data that may aid in housing policy development.
Ecological and cross-sectional research yield valuable insights into public health; however, each possesses unique applications, advantages, and limitations. Ecological studies, illustrated by Whittle and Diaz-Artiles, are suitable for analyzing broad trends and correlations among populations, whereas cross-sectional studies, such as that by Spector et al., are beneficial for acquiring a thorough assessment of a population's health at a specific point.
Both approaches can assist in public health planning and interventions; however, the study's conclusions and efficacy in addressing health issues are contingent upon the design used. Given their objectives, the researchers' choices in both studies seem appropriate; however, we must recognize the limitations of each design.
References
Spector, A. L., Quinn, K. G., McAuliffe, T. L., DiFranceisco, W., Bendixen, A., & Dickson-Gomez, J. (2020). Health-related quality of life and related factors among chronically homeless adults living in different permanent supportive housing models: a cross-sectional study. Quality of Life Research, 29(8), 2051–2061. https://doi.org/10.1007/s11136-020-02482-wLinks to an external site.
Martha Ngenue
Sep 10 12:17pm
Reply from Martha Ngenue
Observational Study Designs: Strengths and Limitations
This week, I reviewed two observational studies that explored the relationship between environmental exposures and pediatric respiratory health. Both investigations relied on non-experimental approaches to examine how lifestyle and environmental conditions may be connected to asthma among children.
The first study applied a case-control design, comparing children diagnosed with asthma to those without the condition. Information on exposures such as secondhand smoke, family history, and air quality was obtained from caregiver surveys and medical records. One advantage of this design is that it allows researchers to study multiple exposures related to a relatively uncommon outcome, like asthma, without the need for long-term follow-up. However, one drawback is the potential for recall bias, since parents of children with asthma may be more likely to remember and report exposures than parents of healthy children (Setia, 2016). The study population was composed of Hispanic children in an urban community, with surveys and health records serving as the primary data sources. The key epidemiologic measure was the odds ratio. Overall, this method was well suited for examining the potential link between exposures and asthma diagnosis.
The second study used a cross-sectional design to measure asthma prevalence and associated risk factors at one point in time. Researchers collected data on exposures such as tobacco smoke, obesity, and early respiratory infections using structured questionnaires. A major strength of this design is that it provides a snapshot of both exposures and outcomes simultaneously, which is useful for estimating prevalence and generating hypotheses. Its limitation, however, lies in the inability to determine temporality, making it unclear whether exposures occurred before the onset of asthma (Levin, 2006). The study population included school-aged children from several neighborhoods, and the prevalence ratio was used as the main measure of association. Although this design does not establish causality, it was an appropriate approach to assess the burden of asthma in a community and to highlight possible contributing factors.
In both studies, the chosen designs were appropriate for the research questions being addressed. The case-control design was effective for identifying possible associations, while the cross-sectional design provided a broader view of prevalence within the population. Nonetheless, stronger causal conclusions would require a cohort design, which is better suited to tracking exposures over time. Together, these studies offer valuable evidence that can inform community-based strategies aimed at reducing childhood asthma.
References
Levin, K. A. (2006). Study design III: Cross-sectional studies. Evidence-Based Dentistry, 7(1), 24–25. https://doi.org/10.1038/sj.ebd.6400375
Setia, M. S. (2016). Methodology series module 2: Case-control studies. Indian Journal of Dermatology, 61(2), 146–151. https://doi.org/10.4103/0019-5154.177773
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