Literature Collection

BACKGROUND: Incorporation of cognitive screening into the busy primary care will require the development of highly efficient screening tools. We report the convergence validity of a very brief, self-administered, computerized assessment protocol against one of the most extensively used, clinician-administered instruments-the Montreal Cognitive Assessment (MoCA). METHOD: Two hundred six participants (mean age = 67.44, standard deviation [SD] = 11.63) completed the MoCA and the computerized test. Three machine learning algorithms (ie, Support Vector Machine, Random Forest, and Gradient Boosting Trees) were trained to classify participants according to the clinical cutoff score of the MoCA (ie, /=26, n = 165), suggesting greater sensitivity to age-related changes in cognitive functioning. CONCLUSION: Future studies should examine ways to improve the sensitivity of the computerized test by expanding the cognitive domains it measures without compromising its efficiency.

Cardiovascular disease continues to cause an important global health challenge, highlighting the critical importance of early detection in mitigating cardiac-related issues. There is a significant demand for reliable diagnostic alternatives. Taking advantage of health data through diverse machine learning algorithms may offer a more precise diagnostic approach. Machine learning-based decision support systems that utilize patients' clinical parameters present a promising solution for diagnosing cardiovascular disease. In this research, we collected extensive publicly available healthcare records. We integrated medical datasets based on common features to implement several machine learning models aimed at exploring the potential for more robust predictions of cardiovascular disease (CVD). The merged dataset initially contained 323,680 samples sourced from multiple databases. Following data preprocessing steps including cleaning, alignment of features, and removal of missing values, the final dataset consisted of 311,710 samples used for model training and evaluation. In our experiments, the CatBoost model achieved the highest area under the curve (AUC) of up to 94.1%.

AIM: This developmental study tested the feasibility of training pharmacy staff on the psychologically informed environments (PIE) approach to improve the delivery of care. BACKGROUND: Community pharmacies provide key services to people who use drugs (PWUD) through needle exchange services, medication-assisted treatment and naloxone distribution. PWUD often have trauma backgrounds, and an approach that has been demonstrated to work well in the homeless sector is PIEs. METHODS: Bespoke training was provided by clinical psychologists and assessed by questionnaire. Staff interviews explored changes made following PIE training to adapt the delivery of care. Changes in attitude of staff following training were assessed by questionnaire. Peer researchers interviewed patient/client on observed changes and experiences in participating pharmacies. Staff interviews were conducted six months after training to determine what changes, if any, staff had implemented. Normalisation process theory (NPT) provided a framework for assessing change. FINDINGS: Three pharmacies (16 staff) participated. Training evaluation was positive; all participants rated training structure and delivery as 'very good' or 'excellent'. There was no statistically significant change in attitudes. COVID-19 lockdowns restricted follow-up data collection. Staff interviews revealed training had encouraged staff to reflect on their practice and communication and consider potentially discriminatory practice. PIE informed communication skills were applied to manage COVID-19 changes. Staff across pharmacies noted mental health challenges for patients. Five patients were interviewed but COVID-19 delays in data collection meant changes in delivery of care were difficult to recall. However, they did reflect on interactions with pharmacy staff generally. Across staff and patient interviews, there was possible conflation of practice changes due to COVID-19 and the training. However, the study found that training pharmacy teams in PIE was feasible, well received, and further development is recommended. There was evidence of the four NPT domains to support change (coherence, cognitive participation, collective action and reflexive monitoring).