Quantitative Articles in Healthcare

Article Analysis
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Article Analysis 1
Article Citation and Permalink (APA format)

Article 1
Pelletier, J. E., Laska, M. N., MacLehose, R., Nelson, T. F., & Nanney, M. S. (2017). Evidence-based school nutrition and physical education policies: Associations with state-level collaboration, obesity, and socio-economic indicators. Preventive Medicine, pp. 99, 87–93. https://doi.org/10.1016/j.ypmed.2017.02.005

Article 2
Hogg, R. A., & Varda, D. (2016). Insights Into Collaborative Networks Of Nonprofit, Private, And Public Organizations That Address Complex Health Issues. Health Affairs, 35(11), 2014–2019. https://doi.org/10.1377/hlthaff.2016.0725

Article 3
Robbins, J. M., Mallya, G., Wagner, A., & Buehler, J. W. (2015). Prevalence, Disparities, and Trends in Obesity and Severe Obesity Among Students in the School District of Philadelphia, Pennsylvania, 2006–2013. Preventing Chronic Disease, 12. https://doi.org/10.5888/pcd12.150185

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Description

Description

Description

Broad Topic Area/Title

Evidence-based policies on school nutrition and physical education: Associations with state-level collaboration, obesity, and socio-economic indicators

Insights Into Collaborative Networks Of Nonprofit, Private, And Public Organizations That Address Complex Health Issues

Prevalence, Disparities, and Trends in Obesity and Severe Obesity Among Students in the School District of Philadelphia, Pennsylvania, 2006–2013

Identify Independent and Dependent Variables and Type of Data for the Variables

Independent Variables.
Categorical valuable. Collaborative Partnerships
Continuous variable. Childhood Obesity Prevalence
Continuous variable. High-School Non-Completion Prevalence
Continuous variable. Poverty Prevalence
Dependent Variables.
Strong Competitive Food/Beverage (CF) Policies - Categorical variable
Strong Physical Education (PE) Policies - Categorical variable

Independent Factors
Organization Type: The type of organization taking part in cross-sector community health networks is represented by this variable. The article divides organizations into many sorts, including nonprofits, regional government entities, educational institutions, and health insurers.
Data type: Nominal or ordinal categorical data representing various organizational type categories.
Dependent variables:
Resources Contributed: This variable counts and categorizes the resources that different organizations add to their networks.
Data type: Numerical (discrete) data that counts the resources donated. Additionally, category data illustrates the various resources (such as social ties, medical knowledge).

independent variable
School Years 2006–2007 to 2012–2013) The many periods that the study was done are represented by this variable. It is used to evaluate trends and shifts in the prevalence of pediatric obesity.
Dependent Variables:
Prevalence of Childhood Obesity: This is the major outcome indicator of interest. It represents the percentage of children aged 5 to 18 with a BMI greater than or equal to the 95th median. It is calculated throughout different time frames and among diverse demographic groupings.

The Population of Interest for the Study

The population of interest for the study, as mentioned in the provided article, is "all 50 states and the District of Columbia, USA." (Hogg & Varda, 2016). This implies that the study is focused on examining the associations and factors related to collaborative partnerships and state-level policies for obesity prevention across all states in the USA.