Τετάρτη 18 Μαΐου 2022

Reducing gender differences in student motivational‐affective factors: A meta‐analysis of school‐based interventions

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Abstract

Background

Research shows that gender differences tend to exist in student motivational-affective factors in core subjects such as math, science or reading, where one gender is stereotypically disadvantaged.

Aims

This study aimed to investigate strategies that could reduce these gender differences by conducting a meta-analysis on school-based intervention studies that targeted student motivational-affective factors. We therefore evaluated whether interventions had differential effects for male and female students' motivational-affective factors in a given academic subject. We also evaluated potential moderator variables.

Method

After conducting a systematic database search and screening abstracts for inclusion, we synthesized 71 effect sizes from 20 primary studies. All included studies were conducted in science or mathematics-related subjects, which are stereotypically female-disadvantaged.

Results

While the interventions had significant positive effects for both genders, there was no statistically significant difference between the two genders with regard to the intervention effects on motivational-affective factors. However, the descriptive effect size for female students (g = .49) was far greater than for male students (g = .28). Moderator analyses showed no significant effects for grade level, intervention duration, or school subject, but there was a significant influence of intervention method used.

Conclusions

This study demonstrated that school-based interventions have positive effects on motivational-affective factors for both genders. It also provides evidence that interventions in subjects where female students are stereotypically disadvantaged may have greater effects for females than for males. Implications and suggestions for future research are discussed.

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High flow nasal cannula in the management of obstructive sleep Apnoea postoperatively. Is flow a new alternative to positive pressure?

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Publication date: Available online 17 May 2022

Source: American Journal of Otolaryngology

Author(s): Abhijit S. Nair, Antonio M. Esquinas

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Association between sperm mitochondrial DNA copy number and deletion rate and industrial air pollution dynamics

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Changes in global DNA methylation under climatic stress in two related grasses suggest a possible role of epigenetics in the ecological success of polyploids

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Total inferior border ostectomy versus T-shape genioplasty for chin narrowing combined with mandibular contouring

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The objective of this study was to compare the indications and outcomes of the total inferior border ostectomy and T-shape genioplasty. A retrospective study was conducted using the clinical notes and records of patients who underwent total inferior border ostectomy (group 1, n  = 42) and T-shape genioplasty (group 2, n = 60). The outcomes were evaluated by assessment of computed tomography images combined with medical records and photographs. Lower facial height, chin width, chin symmetry, and facial proportions, as well as patient satisfaction and complications were investigated. (Source: International Journal of Oral and Maxillofacial Surgery)
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Machine Learning Based Forecast of Dengue Fever in Brazilian Cities using Epidemiological and Meteorological Variables

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Abstract
Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we develop a model to predict monthly dengue cases in Brazilian cities one month ahead from 2007-2019. We compare different machine learning algorithms and feature selection methods using epidemiological and meteorological variables. We find that different models work best in different cities, and a random forests model trained on monthly dengue cases performs best overall. It produces lower errors than a seasonal naïve baseline model, gradient boosting regression, feed-forward neural network, and support vector regression. For each city, we compute the mean absolute error between predictions and true monthly dengue cases on the test set. For the median city, the error is 1 2.2 cases. This error is reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities.
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