Conectar
by Nicodemus Songose Awarayi, Frimpong Twum, James Ben Hayfron-Acquah, Kwabena Owusu-Agyemang
This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. Subsequently, a convolutional neural network model comprising four convolutional layers and two hidden layers was devised for classifying Alzheimer’s disease into three (3) distinct categories, namely mild cognitive impairment, Alzheimer’s disease, and normal controls. The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. The proposed model yielded notable results, such as an accuracy of 93.45% and an area under the curve value of 0.99 when trained on the three classes. The model further showed superior results on binary classification compared with existing methods. The model recorded 94.39%, 94.92%, and 95.62% accuracies for Alzheimer’s disease versus normal controls, Alzheimer’s disease versus mild cognitive impairment, and mild cognitive impairment versus normal controls classes, respectively.To investigate how demographic, contractual and organisational factors are related to the retention of hospital workers in the English NHS. The study will specifically examine the trends in age-retention profiles.
A double retrospective cross-cohort study using administrative data on senior and specialty doctors, nurses and midwives who were included in the 2009 and 2014 payrolls of all English NHS hospital Trusts. These individuals were tracked over time until 2019 to examine the associations between sociodemographic characteristics and the retention of hospital workers in each cohort. Logistic regressions were estimated at the individual worker level to analyse the data. Additionally, a multilevel panel regression was performed using linked payroll-survey data to investigate the association between hospital organisation characteristics and the retention of clinical staff.
Secondary acute and mental healthcare NHS hospital Trusts in England.
70 777 senior doctors (specialty and specialist doctors and hospital consultants) aged 30–70, and a total of 448 568 between nurses and midwives of any grade aged 20–70, employed by English NHS Trusts.
Employee retention, measured through binary indicators for stayers and NHS leavers, at 1-year and 5-year horizons.
Minority doctors had lower 1-year retention rates in acute care than white doctors, while minority nurses and midwives saw higher retention. Part-time roles decreased retention for doctors but improved it for nurses. Fixed-term contracts negatively impacted both groups’ retention. Trends diverged for nurses and doctors from 2009 to 2014—nurses’ retention declined while doctors’ 5-year retention slightly rose. Engagement boosted retention among clinical staff under 51 years of age in acute care. For nurses over 50, addressing their feedback was positively associated with retention.
Demographic and contractual factors appear to be stronger predictors of hospital staff retention than organisational characteristics.
by Michala Sliefert, May Maloba, Catherine Wexler, Frederick Were, Yvonne Mbithi, George Mugendi, Edward Maliski, Zachary Nicolay, Gregory Thomas, Shadrack Kale, Nicodemus Maosa, Sarah Finocchario-Kessler
BackgroundCurrent formulations of pediatric antiretroviral therapy (ART) for children with HIV present significant barriers to adherence, leading to drug resistance, ART ineffectiveness, and preventable child morbidity and mortality. Understanding these challenges and how they contribute to suboptimal adherence is an important step in improving outcomes. This qualitative study describes how regimen-related challenges create barriers to adherence and impact families.
MethodsWe conducted key informant interviews (KIIs) with 30 healthcare providers and 9 focus group discussions (FGDs) with a total of 72 caregivers, across three public hospitals in Siaya and Mombasa Kenya. The KIIs and FGDs were audio recorded, translated, and transcribed verbatim. The transcripts were hand coded based on emergent and a-priori themes.
ResultsCaregivers discussed major regimen-related challenges to adherence included poor palatability of current formulations, complex preparation, and administration (including measuring, crushing, dissolving, mixing), complex drug storage, and frequent refill appointments and how these regimen-related challenges contributed to individual and intrapersonal barriers to adherence. Caregivers discussed how poor taste led to child anxiety, refusal of medications, and the need for caregivers to use bribes or threats during administration. Complex preparation led to concerns and challenges about maintaining privacy and confidentiality, especially during times of travel. Providers corroborated this patient experience and described how these challenges with administration led to poor infant outcomes, including high viral load and preventable morbidity. Providers discussed how the frequency of refills could range from every 2 weeks to every 3 months, depending on the patient. Caregivers discussed how these refill frequencies interrupted work and school schedules, risked unwanted disclosure to peers, required use of financial resources for travel, and ultimately were a challenge to adherence.
ConclusionThese findings highlight the need for improved formulations for pediatric ART to ease the daily burden on caregivers and children to increase adherence, improve child health, and overall quality of life of families.
FreshRSS 