Publication and characteristics of qualitative research in School Psychology journals between 2006 and 2021.

To understand the evolution and current status of qualitative research in School Psychology, we reviewed 4,346 articles published across seven school psychology journals between 2006 and 2021. The bibliometric analysis indicates that publication of qualitative research has increased over the years, but remains small (3%) when seen against the total volume of journal publications. Less than 5% of articles in all but one journal were qualitative. The most commonly explored topic was diversity, equity, and social justice accounting for 23% of the qualitative articles. In total, 55% of the studies were conducted in the United States. Although many studies did not specify participants' race and gender, the most commonly reported research participants were K-12 students, female, White, and from the United States. We discuss these findings and provide recommendations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS).

OBJECTIVE: We aimed to develop a model for accurate prediction of general care inpatient deterioration. MATERIALS AND METHODS: Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call. RESULTS: Training, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score. DISCUSSION: Low alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering. CONCLUSIONS: MC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.

Identifying patients at risk of inhospital death or hospice transfer for early goals of care discussions in a US referral center: the HELPS model derived from retrospective data.

OBJECTIVE: Create a score to identify patients at risk of death or hospice placement who may benefit from goals of care discussion earlier in the hospitalisation. DESIGN: Retrospective cohort study to develop a risk index using multivariable logistic regression. SETTING: Two tertiary care hospitals in Southeastern Minnesota. PARTICIPANTS: 92 879 adult general care admissions (50% male, average age 60 years). PRIMARY AND SECONDARY OUTCOME MEASURES: Our outcome measure was an aggregate of inhospital death or discharge to hospice. Predictor variables for the model encompassed comorbidities, nutrition status, functional status, demographics, fall risk, mental status, Charlson Comorbidity Index and acuity of illness on admission. Resuscitation status, race, geographic area of residence and marital status were added as covariates to account for confounding. RESULTS: Inhospital mortality and discharge to hospice were rare, with incidences of 1.2% and 0.8%, respectively. The Hospital End-of-Life Prognostic Score (HELPS) demonstrated good discrimination (C-statistic=0.866 in derivation set and 0.834 in validation set). The patients with the highest 5% of scores had an 8% risk of the outcome measure, relative risk 12.9 (10.9-15.4) when compared to the bottom 95%. CONCLUSIONS: HELPS is able to identify patients with a high risk of inhospital death or need for hospice at discharge. These patients may benefit from early goals of care discussions.