Building a Foundation for Diabetes Clinical Behavioral Research with Incarcerated Persons

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Correctional Nurse Competency and Quality Care Outcomes

Purpose: In this paper, we report on the clinical care outcomes resulting from implementing a nurse competency education intervention to a nursing staff in a statewide correctional system. Background: State correctional healthcare systems face ongoing and serious challenges attracting and retaining an adequate number of qualified health professionals owing to the perceived undesirability of working in correctional facilities; high occupational stressors; and the effects of high turnover on the workloads of remaining nursing staff. Methods: Nursing outcomes were evaluated on four most frequently used nursing protocols. The education intervention consisted of self-directed computer-based modules, hands-on clinical review of skills, and validation of learning through high-fidelity simulation tailored for the correctional environment. A multilevel random coefficient modeling approach for analysis was utilized. Records reviewed at 7 facilities comprising a sample of 736 records were compared for nursing care performance between facilities. Findings: The education intervention as designed increased nurse competency (t= 2.591, df=729, p=0.010) on average 4% across the state system (facility range -4% to +10.8%). A four percent system change has been found to be an effective rate of change in other studies. Facilities with an overall higher RN to LPN ratio perform at a relatively high level (t=4.211, df=730, p=0.000). Increase in inmate census without change in RN/LPN nurse staffing reduced performance (t= -4.347, df=730, p=0.000). Conclusions: This multi-component nursing education intervention improved quality of nursing care, most dramatically in the area of psychiatric care. Structural challenges related to paper charts and security suggest improvements may be seen with an electronic record system and expanded training between nursing and Correctional Officers for health care. An examination of the effectiveness of current models of care delivery may be warranted

Core curricular priorities for professional development of nurses in correctional systems: a Delphi study

Objective: To identify the core curricular elements to assure competency and professional development of registered nurses working with justice-involved populations. Background: Numerous assessments of education priorities have been articulated for nurses working with patients who interface with justice systems. But no consensus of what comprises the core elements of a curriculum for nurses employed by justice systems has been published. Guidance from correctional nurse education experts is needed. Design: A web-based Delphi survey methodology was used. Three de-identified surveys were sent to academic and clinical correctional nurse educators two weeks apart by email following an invitation and voluntary agreement to participate. An IRB waiver was sought and obtained. Setting and participants: Expert educator participants were identified through internet searches of publications, grants and referrals. Participants include 14 nurse academicians who teach correctional health topics, 5 clinical nurse educators employed in correctional settings across the US, and 1 international academician. Results: Thirteen core curricular elements were identified, prioritized and clustered under sub-headings of knowledge, attitudes, and skills. Two types of programming were identified: professional development for new nurses entering correctional systems; and, maintenance of clinical competency. Use of evidence-based educational materials were identified as important. Conclusion: There is consensus that a core curriculum is needed to bring standardization to educational programming for correctional nursing. Identification of a core curricula is a fundamental step toward recognition of the professional expertise required in this forensic nursing sub-specialty. Targeted competency development curricula can reduce costs associated with high rates of attrition, delayed readiness for clinical service, poor quality of care and high error rates and missed care omissions

Psychometric Validation Of Satisfaction With Simulated Clinical Learning Experience Evaluation - Corrections (Ssclee-C)

Purpose This study sought to modify a 19-item instrument designed to measure nursing students\u27 satisfaction with a simulated learning experience for use in a correctional system to measure nurse satisfaction with simulated learning experiences; and to establish validity for the modified instrument. No measures were available for use in a correctional setting, and few instruments were available to measure nurse satisfaction with simulation experiences. Design/methodology One hundred and ninety-eight correctional nurses responded to the original 19-item five-point Likert scale instrument. These data were used for an exploratory and confirmatory factor analysis. Findings A 3-factor solution accounting for 62% of the variance. The 3 factors: Fidelity, Objectives, and Problem solving were supported by simulation theory. The 9-item CFA exhibited desirable psychometric properties: Root mean square error of approximation (RMSEA) =.046; Akaike Information Criterion (AIC) = 76.95; Comparative Fit Index (CFI) =.984. The model χ2 = 30.95 (ns). Alpha reliability estimates of the three factors were 0.70, 0.70 and 0.81. Originality/value The Satisfaction with Simulated Clinical Learning Experience Evaluation - Corrections (SSCLEE-C) is the only instrument available for ongoing assessment of correctional nurse satisfaction with simulated clinical learning experiences

Psychometric validation of satisfaction with simulated clinical learning experience evaluation – corrections (SSCLEE-C)

Purpose: This study sought to modify a 19-item instrument designed to measure nursing students' satisfaction with a simulated learning experience for use in a correctional system to measure nurse satisfaction with simulated learning experiences; and to establish validity for the modified instrument. No measures were available for use in a correctional setting, and few instruments were available to measure nurse satisfaction with simulation experiences. Design/methodology: One hundred and ninety-eight correctional nurses responded to the original 19-item five-point Likert scale instrument. These data were used for an exploratory and confirmatory factor analysis. Findings: A 3-factor solution accounting for 62% of the variance. The 3 factors: Fidelity, Objectives, and Problem solving were supported by simulation theory. The 9-item CFA exhibited desirable psychometric properties: Root mean square error of approximation (RMSEA) = .046; Akaike Information Criterion (AIC) = 76.95; Comparative Fit Index (CFI) = .984. The model χ2 = 30.95 (ns). Alpha reliability estimates of the three factors were 0.70, 0.70 and 0.81. Originality/value: The Satisfaction with Simulated Clinical Learning Experience Evaluation – Corrections (SSCLEE-C) is the only instrument available for ongoing assessment of correctional nurse satisfaction with simulated clinical learning experiences. Keywords: Simulation, Satisfaction, Correctional nurses, Instrument developmen

Nurse Practitioners caring for incarcerated people disproportionately impacted by social determinants of health.

This article explores roles filled and care provided by NPs in diverse carceral settings along with the impact of health disparities experienced as a result of social determinants of health on the chronic disease burden of people who are incarcerated. Opportunities abound for NPs to positively influence this patient population\u27s health, but specialized education is needed

Use of machine-learning algorithms to aid in the early detection of leptospirosis in dogs

Leptospirosis is a life-threatening, zoonotic disease with various clinical presentations, including renal injury, hepatic injury, pancreatitis, and pulmonary hemorrhage. With prompt recognition of the disease and treatment, 90% of infected dogs have a positive outcome. Therefore, rapid, early diagnosis of leptospirosis is crucial. Testing for Leptospira-specific serum antibodies using the microscopic agglutination test (MAT) lacks sensitivity early in the disease process, and diagnosis can take >2 wk because of the need to demonstrate a rise in titer. We applied machine-learning algorithms to clinical variables from the first day of hospitalization to create machine-learning prediction models (MLMs). The models incorporated patient signalment, clinicopathologic data (CBC, serum chemistry profile, and urinalysis = blood work [BW] model), with or without a MAT titer obtained at patient intake (=BW + MAT model). The models were trained with data from 91 dogs with confirmed leptospirosis and 322 dogs without leptospirosis. Once trained, the models were tested with a cohort of dogs not included in the model training (9 leptospirosis-positive and 44 leptospirosis-negative dogs), and performance was assessed. Both models predicted leptospirosis in the test set with 100% sensitivity (95% CI: 70.1-100%). Specificity was 90.9% (95% CI: 78.8-96.4%) and 93.2% (95% CI: 81.8-97.7%) for the BW and BW + MAT models, respectively. Our MLMs outperformed traditional acute serologic screening and can provide accurate early screening for the probable diagnosis of leptospirosis in dogs