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The aim of this study to create a comprehensive overview of factors that may influence motivation of ethnic minority students from their own perspective. A systematic review was conducted in PubMed, ERIC, and PsycINFO to find studies in which the motivation of ethnic minority students was studied. The articles reviewed were qualitatively synthesized by means of meta-ethnography, and were subjected to a quantitative meta-analysis where appropriate. Forty-five articles were included. Several factors were found to have either a positive or a negative influence on academic motivation, which can be classified into individual, family-related, school-related, and social factors. These factors should be taken into account when developing interventions aimed at enhancing motivation, which is expected to improve. However, evidence for the influence of most identified factors is weak, given that almost every factor was investigated in a single study only. Based on the outcomes of the current overview an integrative model, that provides a structure of the identified factors in relation to motivation which can be used for interventions, cannot be generated; thus, further research is needed.

Background: Chronic cardiometabolic diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD), share many modifiable risk factors and can be prevented using combined prevention programs. Valid risk prediction tools are needed to accurately identify individuals at risk.Objective: We aimed to validate a previously developed non-invasive risk prediction tool for predicting the combined 7-year-risk for chronic cardiometabolic diseases.Design: The previously developed tool is stratified for sex and contains the predictors age, BMI, waist circumference, use of antihypertensives, smoking, family history of myocardial infarction/stroke, and family history of diabetes. This tool was externally validated, evaluating model performance using area under the receiver operating characteristic curve (AUC)-assessing discrimination-and Hosmer-Lemeshow goodness-of-fit (HL) statistics-assessing calibration. The intercept was recalibrated to improve calibration performance.Participants: The risk prediction tool was validated in 3544 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab).Key Results: Discrimination was acceptable, with an AUC of 0.78 (95% CI 0.75-0.81) in men and 0.78 (95% CI 0.74-0.81) in women. Calibration was poor (HL statistic: p < 0.001), but improved considerably after intercept recalibration. Examination of individual outcomes showed that in men, AUC was highest for CKD (0.85 [95% CI 0.78-0.91]) and lowest for T2D (0.69 [95% CI 0.65-0.74]). In women, AUC was highest for CVD (0.88 [95% CI 0.83-0.94)]) and lowest for T2D (0.71 [95% CI 0.66-0.75]).Conclusions: Validation of our previously developed tool showed robust discriminative performance across populations. Model recalibration is recommended to account for different disease rates. Our risk prediction tool can be useful in large-scale prevention programs for identifying those in need of further risk profiling because of their increased risk for chronic cardiometabolic diseases. [ABSTRACT FROM AUTHOR]

Purpose: Healthcare providers often experience difficulties in discussing depression with adults with visual impairment (VI), obstructing timely referral. The purpose of this study was to examine predictors of routine discussions of depression with adults with VI from the perspective of different healthcare providers from different countries., Methods: Cross-sectional survey data from Welsh (N = 122), Australian (N = 94) and Dutch (N = 100) healthcare providers, that is eye care practitioners (ECPs) and low-vision care providers (LVCPs), were analysed. Multivariable logistic regression analysis was performed in the Welsh sample to determine predictors for discussing depression. Internal validation was conducted by using a bootstrap method, and the recalibrated model was externally validated in the Australian and Dutch sample., Results: Work experience in eye care services (OR 0.95; 95% confidence interval (CI) 0.92 to 0.99) and perceived barriers (OR 0.95; 95% CI 0.92 to 0.98) was found to predict discussing depression with patients. The area under the curve (AUC) of 0.73 reflected good discrimination of the model. The model showed a slightly better fit in the Australian sample (AUC = 0.77), but a poor fit in the Dutch sample., Conclusion: The final prediction model was not generalizable to Dutch healthcare providers. They perceived less barriers in depression management than Welsh and Australian healthcare providers. This could be explained by differences in ECPs and LVCPs roles and responsibilities, increased attention on mental health and differences in organizing health care. Differences between healthcare providers' responsibilities and support needs should be taken into account while creating a facilitating environment to discuss depression., (© 2023 The Authors. Acta Ophthalmologica published by John Wiley & Sons Ltd on behalf of Acta Ophthalmologica Scandinavica Foundation.)

Background: Neck pain is one of the leading causes of disability in most countries and it is likely to increase further. Numerous prognostic models for people with neck pain have been developed, few have been validated. In a recent systematic review, external validation of three promising models was advised before they can be used in clinical practice., Objective: The purpose of this study was to externally validate three promising models that predict neck pain recovery in primary care., Methods: This validation cohort consisted of 1311 patients with neck pain of any duration who were prospectively recruited and treated by 345 manual therapists in the Netherlands. Outcome measures were disability (Neck Disability Index) and recovery (Global Perceived Effect Scale) post-treatment and at 1-year follow-up. The assessed models were an Australian Whiplash-Associated Disorders (WAD) model (Amodel), a multicenter WAD model (Mmodel), and a Dutch non-specific neck pain model (Dmodel). Models' discrimination and calibration were evaluated., Results: The Dmodel and Amodel discriminative performance (AUC < 0.70) and calibration measures (slope largely different from 1) were poor. The Mmodel could not be evaluated since several variables nor their proxies were available., Conclusions: External validation of promising prognostic models for neck pain recovery was not successful and their clinical use cannot be recommended. We advise clinicians to underpin their current clinical reasoning process with evidence-based individual prognostic factors for recovery. Further research on finding new prognostic factors and developing and validating models with up-to-date methodology is needed for recovery in patients with neck pain in primary care., Competing Interests: Conflict of Interest The authors declare no conflicts of interest, (Copyright © 2021 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.)

Background: Chronic cardiometabolic diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD), share many modifiable risk factors and can be prevented using combined prevention programs. Valid risk prediction tools are needed to accurately identify individuals at risk., Objective: We aimed to validate a previously developed non-invasive risk prediction tool for predicting the combined 7-year-risk for chronic cardiometabolic diseases., Design: The previously developed tool is stratified for sex and contains the predictors age, BMI, waist circumference, use of antihypertensives, smoking, family history of myocardial infarction/stroke, and family history of diabetes. This tool was externally validated, evaluating model performance using area under the receiver operating characteristic curve (AUC)-assessing discrimination-and Hosmer-Lemeshow goodness-of-fit (HL) statistics-assessing calibration. The intercept was recalibrated to improve calibration performance., Participants: The risk prediction tool was validated in 3544 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab)., Key Results: Discrimination was acceptable, with an AUC of 0.78 (95% CI 0.75-0.81) in men and 0.78 (95% CI 0.74-0.81) in women. Calibration was poor (HL statistic: p < 0.001), but improved considerably after intercept recalibration. Examination of individual outcomes showed that in men, AUC was highest for CKD (0.85 [95% CI 0.78-0.91]) and lowest for T2D (0.69 [95% CI 0.65-0.74]). In women, AUC was highest for CVD (0.88 [95% CI 0.83-0.94)]) and lowest for T2D (0.71 [95% CI 0.66-0.75])., Conclusions: Validation of our previously developed tool showed robust discriminative performance across populations. Model recalibration is recommended to account for different disease rates. Our risk prediction tool can be useful in large-scale prevention programs for identifying those in need of further risk profiling because of their increased risk for chronic cardiometabolic diseases.