Australian women's health literacy and complex decision-making to use complementary medicines in pregnancy and lactation – a qualitative investigation

2019 ◽  
Vol 6 ◽  
pp. S66
Author(s):  
Larisa Barnes ◽  
Lesley Barclay ◽  
Kirsten McCaffery ◽  
Parisa Aslani
Keyword(s):  
Decision Making ◽  
Health Literacy ◽  
Women’S Health ◽  
Women's Health ◽  
Qualitative Investigation ◽  
Complementary Medicines ◽  
Pregnancy And Lactation ◽  
Complex Decision Making ◽  
Complex Decision ◽  
In Pregnancy

scholarly journals Supplementation in pregnancy: determinants of the compliance with guidelines in Portugal, 2019

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
C Ribeiro ◽  
D a Silva ◽  
E Pinto
Keyword(s):  
Folic Acid ◽  
Health Literacy ◽  
Antenatal Care ◽  
Women’S Health ◽  
Women's Health ◽  
Adverse Outcomes ◽  
World Health ◽  
Clinical Practices ◽  
Iodine Supplementation ◽  
In Pregnancy

Abstract Background Folic acid (FA) and iodine supplementation in pregnancy are nutritional interventions recommended by the World Health Organization, which are widespread in Portugal. Since there isn`t available reimbursed medicine containing only 400µg of FA and the increasing evidence that excessive supplementation in FA can lead to adverse outcomes for mothers and their offspring, make interesting to assess the clinical practice. Therefore, it`s important to understand the actual adherence to FA and iodine supplementation in periconceptional period, as well as to identify its determinants. Methods Telephonic interviews (n = 140) were conducted with mothers enrolled in Primary Health Centers, recruited in their children's vaccination appointment (aged 0-6 months). Among the variables studied are sociodemographic, medical and obstetric history, diet and supplements used during pregnancy. Data were analyzed using the Statistical Package for Social Sciences® program. Results The prevalence of iodine and FA supplementation among mothers was high (98%). About 3/4 of the interviewees reported having planned their pregnancy, and of these, 1/4 did not have a preconception consultation. The first pregnancy consultation took place at 6-10 weeks, on average. FA supplementation has the greatest discrepancies with the guidelines: only 29% (n = 40) started at the recommended time, 3% (n = 4) stopped at 12 weeks of pregnancy, and only 33% (n = 34) had 400µg of FA. Fifty one percent of the mothers indicated that they were unaware that they should start supplementation before the interruption of the contraceptive method. Conclusions These results suggest the need for enhanced antenatal care and interventions that increase women's health literacy. Public Health strategies in order to increase preconception consultations are needed as well as a national joint effort towards the homogenization of clinical practices regarding the prescription of supplementation in pregnancy. Key messages Folic acid (FA) and iodine supplementation in pregnancy are widespread practices in Portugal despite the fact that no available reimbursed medicine containing the recommended 400 µg of FA is available. Antenatal care and women's health literacy should be improved in order to comply with the recommendations, as well as the availability of a reimbursed medicine containing 400µg of folic acid.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

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