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The persistent understaffing of the governmental public health workforce has led to program cutbacks, staff burnout at local health departments (LHDs), and an urgent need to replenish staffing. To build recruitment pathways into LHDs and build their workforce capacity, we introduced a paid internship initiative connecting Master's in Public Health students from a Midwestern university with LHDs in the state. This article presents the pilot program developed and the insights gained from it. Program participants included nine LHDs that hosted 10 students for 12-week internships. Internship projects were developed by LHDs with support from the state's association of county and city health officials. All students completed their internship projects satisfactorily. The experience highlighted that while students contributed to LHDs through short-term projects, with sustained backing and minor adjustments, this model can serve to reinforce the governmental public health sector's existing and future capacity in the long term.

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BACKGROUND: Public health nurses (PHNs) engage in home visiting services and documentation of care services for at-risk clients. To increase efficiency and decrease documentation burden, it would be useful for PHNs to identify critical data elements most associated with patient care priorities and outcomes. Machine learning techniques can aid in retrospective identification of critical data elements. OBJECTIVE: We used two different machine learning feature selection techniques of minimum redundancy-maximum relevance (mRMR) and LASSO (least absolute shrinkage and selection operator) and elastic net regularized generalized linear model (glmnet in R). METHODS: We demonstrated application of these techniques on the Omaha System database of 205 data elements (features) with a cohort of 756 family home visiting clients who received at least one visit from PHNs in a local Midwest public health agency. A dichotomous maternal risk index served as the outcome for feature selection. APPLICATION: Using mRMR as a feature selection technique, out of 206 features, 50 features were selected with scores greater than zero, and generalized linear model applied on the 50 features achieved highest accuracy of 86.2% on a held-out test set. Using glmnet as a feature selection technique and obtaining feature importance, 63 features had importance scores greater than zero, and generalized linear model applied on them achieved the highest accuracy of 95.5% on a held-out test set. DISCUSSION: Feature selection techniques show promise toward reducing public health nursing documentation burden by identifying the most critical data elements needed to predict risk status. Further studies to refine the process of feature selection can aid in informing PHNs' focus on client-specific and targeted interventions in the delivery of care.

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Public health clinical educators and practicing public health nurses (PHNs) are experiencing challenges in creating meaningful clinical learning experiences for nursing students due to an increase in nursing programs and greater workload responsibilities for both nursing faculty and PHNs. The Henry Street Consortium (HSC), a collaborative group of PHNs and nursing faculty, conducted a project to identify best practices for public health nursing student clinical learning experiences. Project leaders surveyed HSC members about preferences for teaching-learning strategies, facilitated development of resources and tools to guide learning, organized faculty/PHN pilot teams to test resources and tools with students, and evaluated the pilot team experiences through two focus groups. The analysis of the outcomes of the partnership engagement project led to the development of the Partnership Engagement Model (PEM), which may be used by nursing faculty and their public health practice partners to guide building relationships and sustainable partnerships for educating nursing students.

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OBJECTIVE: To examine the associations between social and behavioral determinants of health (SBDH), health disparities, and the outcomes of women who received public health nurse home visits for pregnancy and parenting support. DESIGN: Observational exploratory data analysis and comparative outcome evaluation. SETTING: An extant dataset from women served in a Midwestern U.S. state, including demographics and Omaha System problems, signs/symptoms, interventions, and outcome assessments. PARTICIPANTS: Women (N = 4,263) with an average age of 23.6 years (SD = 6.1); 21.4% were married, and 39.1% were White. METHODS: An evaluation dataset was constructed that included all women of childbearing age, their demographics, and outcome assessments. A summative SBDH Index based on Institute of Medicine-recommended instruments was computed based on sign/symptom data. Visualizations were developed using Microsoft Excel, and outcome significance statistics were computed using SPSS version 22 and SAS version 9.4. RESULTS: Outcome evaluation showed positive, significant changes from baseline after public health nurse intervention. Visualization showed variable concentrations of problem-specific signs/symptoms by SBDH Index subgroups. There were between-group differences in overall outcome attainment across SBDH Index subgroups. Compared with White women, minority women had greater improvement; however, despite these gains overall minority final ratings were lower. CONCLUSION: An informatics approach showed that SBDH are important factors for understanding a comprehensive and holistic view of health and health care outcomes. There is potential to use large datasets to further explore intervention effectiveness and progress toward health equity related to SBDH.

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Preparation of the public health nursing (PHN) workforce requires public health nurses from academia and practice to collaborate. However, a shortage of PHN clinical sites may lead to competition between schools of nursing for student placements. The Henry Street Consortium, a group of 5 baccalaureate schools of nursing and 13 local health departments in the state of Minnesota, developed a model for collaboration between PHN education and practice. This paper describes the development process--the forming, storming, norming, and performing stages--experienced by the Henry Street Consortium members. The consortium developed a set of entry-level core PHN competencies that are utilized by both education and practice. It developed menus of learning opportunities that were used to design population-based PHN clinical experiences. In addition, the consortium created a model for training and sustaining a preceptor network. The members of the Henry Street Consortium collaborated rather than competed, used consensus for decision making, and respected and accepted different points of view. This collaboration significantly impacted how schools of nursing and local health departments work together. The consortium's ability to retain its relevance, energy, and momentum for both academic and agency partners sustains the collaboration.

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