APY563851_supplemental_material – Supplemental material for The structure and reliability of the Health of the Nation Outcome Scales

Supplemental material, APY563851_supplemental_material for The structure and reliability of the Health of the Nation Outcome Scales by Barry Speak and Steven Muncer in Australasian Psychiatr

Weight Loss After Weight-Loss Surgery: The Mediating Role of Dichotomous Thinking

This dataset pertains to a cross-sectional investigation encompassing 129 individuals who have undergone weight-loss surgery. It features pre- and post-surgical measurements of weight and body mass index (BMI), alongside assessments of dietary restraint and dichotomous thinking. Analysis of the data substantiates the assertion that dichotomous thinking concerning food and diet acts as a mediator in the association between dietary restraint and the maintenance of weight loss subsequent to surgery. This finding holds clinical significance due to the measurable and modifiable nature of dichotomous thinking through psychological intervention. For further information, please refer to Marshall, Reay, & Bowman's (2024) work titled "Weight Loss After Weight-Loss Surgery: The Mediating Role of Dichotomous Thinking," published in Obesity Surgery. Note that the dataset in question contains no personally identifiable information, and participants provided informed consent for the dissemination of their de-identified research data in support of open science initiatives

The Effects of Day of the Week on Temporal Ambiguity Resolution

Previous research has suggested that individuals’ resolution of temporal ambiguity relies on experiences in physical domains, as well as on future event valence and emotional experiences. In the current study, we investigate whether the interpretation of a temporally ambiguous question is modulated by the day of the week on which the study was conducted. We asked participants (N = 208) to resolve the ambiguous time question on different days of the week (Monday or Friday). The results of the experiment indicate differences in processing of temporal ambiguity between different days of the week. The study raises the interesting possibility that days of the week can have important implications for resolving temporal ambiguity

The Effects of Day of the Week on Temporal Ambiguity Resolution

Previous research has suggested that individuals’ resolution of temporal ambiguity relies on experiences in physical domains, as well as on future event valence and emotional experiences. In the current study, we investigate whether the interpretation of a temporally ambiguous question is modulated by the day of the week on which the study was conducted. We asked participants (N = 208) to resolve the ambiguous time question on different days of the week (Monday or Friday). The results of the experiment indicate differences in processing of temporal ambiguity between different days of the week. The study raises the interesting possibility that days of the week can have important implications for resolving temporal ambiguity

Paterson_OnlineAppendix – Supplemental material for The Short and Longer Term Impacts of Hate Crimes Experienced Directly, Indirectly, and Through the Media

Supplemental material, Paterson_OnlineAppendix for The Short and Longer Term Impacts of Hate Crimes Experienced Directly, Indirectly, and Through the Media by Jenny L. Paterson, Rupert Brown and Mark A. Walters in Personality and Social Psychology Bulleti

sj-pdf-1-prx-10.1177_0033294120979686 - Supplemental material for The Effects of Day of the Week on Temporal Ambiguity Resolution

Supplemental material, sj-pdf-1-prx-10.1177_0033294120979686 for The Effects of Day of the Week on Temporal Ambiguity Resolution by Srdan Medimorec in Psychological Report

Additional file 2: of Explainable statistical learning in public health for policy development: the case of real-world suicide data

Assessing bias. Figure S1. Plot of standardised predicted values against standardised residuals. Figure S2. Histogram and P-P plot of standardised residuals. (ZIP 91 kb

sj-pdf-1-std-10.1177_0956462420906998 - Supplemental material for Designing an electronic blood-borne virus risk alert to improve uptake of testing

Supplemental material, sj-pdf-1-std-10.1177_0956462420906998 for Designing an electronic blood-borne virus risk alert to improve uptake of testing by Paul van Schaik, Susan Lorrimer and David Chadwick in International Journal of STD & AID

Explainable statistical learning in public health for policy development: the case of real-world suicide data

Abstract Background In recent years, the availability of publicly available data related to public health has significantly increased. These data have substantial potential to develop public health policy; however, this requires meaningful and insightful analysis. Our aim is to demonstrate how data analysis techniques can be used to address the issues of data reduction, prediction and explanation using online available public health data, in order to provide a sound basis for informing public health policy. Methods Observational suicide prevention data were analysed from an existing online United Kingdom national public health database. Multi-collinearity analysis and principal-component analysis were used to reduce correlated data, followed by regression analyses for prediction and explanation of suicide. Results Multi-collinearity analysis was effective in reducing the indicator set of predictors by 30% and principal component analysis further reduced the set by 86%. Regression for prediction identified four significant indicator predictors of suicide behaviour (emergency hospital admissions for intentional self-harm, children leaving care, statutory homelessness and self-reported well-being/low happiness) and two main component predictors (relatedness dysfunction, and behavioural problems and mental illness). Regression for explanation identified significant moderation of a well-being predictor (low happiness) of suicide behaviour by a social factor (living alone), thereby supporting existing theory and providing insight beyond the results of regression for prediction. Two independent predictors capturing relatedness needs in social care service delivery were also identified. Conclusions We demonstrate the effectiveness of regression techniques in the analysis of online public health data. Regression analysis for prediction and explanation can both be appropriate for public health data analysis for a better understanding of public health outcomes. It is therefore essential to clarify the aim of the analysis (prediction accuracy or theory development) as a basis for choosing the most appropriate model. We apply these techniques to the analysis of suicide data; however, we argue that the analysis presented in this study should be applied to datasets across public health in order to improve the quality of health policy recommendations