Evaluation of Training Program for the Maternal and Child Health Workforce at Tulane University

Objectives Despite significant investments in Maternal and Child Health (MCH), the United States still lags behind other countries in key MCH indicators. A well-trained workforce is needed to improve MCH. The Division of MCH Workforce Development of HRSA’s Maternal and Child Health Bureau provides funding to schools of Public Health to support Centers of Excellence in MCH, which is focused on preparing the next generation of MCH leaders through specialized training and mentorship. One such center, the Tulane Center of Excellence in MCH (CEMCH), is housed at the Tulane University School of Public Health and Tropical Medicine. This study evaluated the perceived effectiveness and acceptability of the CEMCH leadership training program. Methods A mixed-methods approach was used, consisting of semi-structured interviews and quantitative surveys which were analyzed through inductive methods based in grounded theory and non-parametric methods respectively. Results Results indicated an overall high level of program satisfaction by all stakeholders. Mentorship and personal attention emerged as an important benefit for both former and current Scholars. The opportunity to gain real-world understanding of MCH work through program activities was an added benefit, although these activities also presented the most challenges. Community stakeholders generally did not view the program as providing immediate organizational benefit, but recognized the distal benefit of contributing to a well-trained MCH workforce. Conclusions for Practice These results will be used to inform other MCH training programs and strengthen Tulane’s CEMCH. A well-trained MCH workforce is essential to improving MCH, and high-quality training its foundation.     

Assessment of Physical Activity of Hospitalised Older Adults: A Systematic Review

Background

The assessment of physical activity levels of hospitalised older people requires accurate and reliable measures. Physical activities that older people in hospital commonly engage in include exercises and walking. Measurement of physical activity levels of older inpatients is essential to evaluate the impact of interventions to improve physical activity levels and to determine associations between physical activity in hospital and other health-related outcome measures.

Objective

To determine which measures are used to measure physical activity of older people in hospital, and to describe their properties and applications.

Method

A systematic review of four databases: Medline, Embase, CINAHL and AMED was conducted for papers published from 1996 to 2016. Inclusion criteria were participants aged ≥ 65 years and studies which included measures of physical activity in the acute medical inpatient setting. Studies which specifically assessed the activity levels of surgical patients or patients with neurological conditions such as stroke or brain injury were excluded. All study designs were included in the review.

Results

18 studies were included from 127 articles selected for full review. 15 studies used objective measures to measure the physical activity of older inpatients: 11 studies used accelerometers and four used direct systematic observations. Seven accelerometers were identified including the StepWatch Activity Monitor, activPAL, GENEActiv, Kenz Lifecorder EX, Actiwatch-L, Tractivity and AugmenTech Inc. Pittsburgh accelerometer. Three studies used a subjective measure (interviews with nurses and patients) to classify patients into low, intermediate and high mobility groups. The StepWatch Activity Monitor was reported to be most accurate at step-counting in patients with slow gait speed or altered gait. The activPAL was reported to be highly accurate at classifying postures.

Conclusion

Physical activity levels of older inpatients can be measured using accelerometers. The accuracy of the accelerometers varies between devices and population-specific validation studies are needed to determine their suitability in measuring physical activity levels of hospitalised older people. Subjective measures are less accurate but can be a practical way of measuring physical activity in a larger group of patients.

     

DXA-Rectified Appendicular Lean Mass: Development of Ultrasound Prediction Models in Older Adults

Objective

Dual-energy X-ray absorptiometry (DXA)-derived appendicular lean soft tissue mass (aLM) is used to diagnose sarcopenia. However, DXA-derived aLM includes non-skeletal muscle components, such as fat-free component of adipose tissue fat cell. These components, if not accounted for, could falsely inflate the aLM in individuals with a high amount of adipose tissue mass. B-mode ultrasound accurately measures muscle size in older adults. We sought to develop regression-based prediction equations for estimating DXA-rectified appendicular lean tissue mass (i.e. DXA-derived aLM minus appendicular fat-free adipose tissue (aFFAT); abbreviated as aLM minus aFFAT) using B-mode ultrasound.

Design

Cross-sectional study.

Measurements

Three hundred and eighty-nine Japanese older adults (aged 60 to 79 years) volunteered in the study. aLM was measured using a DXA, and muscle thickness (MT) was measured using ultrasound at nine sites. An ordinary least-squares multiple linear regression model was used to predict aLM minus aFFAT from sex, age and varying muscle thicknesses multiplied by height. Based on previous studies, we chose to use 4 MT sites at the upper and lower extremities (4-site MT model) and a single site (1-site MT model) at the upper extremity to develop prediction models.

Results

The linear prediction models (4 site MT model; R² = 0.902, adjusted R² = 0.899, and 1-site MT model; R² = 0.868, adjusted R² = 0.866) were found to be stable and accurate for estimating aLM minus aFFAT. Bootstrapping (n=1000) resulted in optimism values of 0.0062 (4-site MT model) and 0.0036 (1-site MT model).

Conclusion

The results indicated that ultrasound MT combined with height, age and sex can be used to accurately estimate aLM minus aFFAT in older Japanese adults. Newly developed ultrasound prediction equations to estimate aLM minus aFFAT may be a valuable tool in population-based studies to assess age-related rectified lean tissue mass loss.

     

Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis?

Background

Identification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e.; sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards.

Methods

A total of 848 older inpatients (mean age, 83.0±7.2 years; 41.8% female) admitted to acute care medical wards in Angers University hospital (France) were included in this study using an observational prospective cohort design. Within 24 hours after admission of older inpatients, nurses performed a bedside clinical assessment. Participants were separated into non-fallers and fallers (i.e.; ≥1 fall during hospitalization stay). The analysis was conducted using three feed forward ANNs (multilayer perceptron [MLP], averaged neural network, and neuroevolution of augmenting topologies [NEAT]).

Results

Seventy-three (8.6%) participants fell at least once during their hospital stay. ANNs showed a high specificity, regardless of which ANN was used, and the highest value reported was with MLP (99.8%). In contrast, sensitivity was lower, with values ranging between 98.4 to 14.8%. MLP had the highest accuracy (99.7).

Conclusions

Performance criteria for fall prediction resulting from a bedside nursing assessment and an ANNs analysis was associated with a high specificity but a low sensitivity, suggesting that this combined approach should be used more as a diagnostic test than a screening test when considering older inpatients in acute care medical ward.

     

The Jefferson Scale of Empathy: a nationwide study of measurement properties,underlying components,latent variable structure,and national norms in medical students

The Jefferson Scale of Empathy (JSE) is a broadly used instrument developed to measure empathy in the context of health professions education and patient care. Evidence in support of psychometrics of the JSE has been reported in health professions students and practitioners with the exception of osteopathic medical students. This study was designed to examine measurement properties, underlying components, and latent variable structure of the JSE in a nationwide sample of first-year matriculants at U.S. colleges of osteopathic medicine, and to develop a national norm table for the assessment of JSE scores. A web-based survey was administered at the beginning of the 2017–2018 academic year which included the JSE, a scale to detect “good impression” responses, and demographic/background information. Usable surveys were received from 6009 students enrolled in 41 college campuses (median response rate?=?92%). The JSE mean score and standard deviation for the sample were 116.54 and 10.85, respectively. Item-total score correlations were positive and statistically significant (p?<?0.01), and Cronbach α?=?0.82. Significant gender differences were observed on the JSE scores in favor of women. Also, significant differences were found on item scores between top and bottom third scorers on the JSE. Three factors of Perspective Taking, Compassionate Care, and Walking in Patient’s Shoes emerged in an exploratory factor analysis by using half of the sample. Results of confirmatory factor analysis with another half of the sample confirmed the 3-factor model. We also developed a national norm table which is the first to assess students’ JSE scores against national data.