“Even though they insult us, the delivery they give us is the greatest thing”: a qualitative study contextualizing women’s experiences with facility-based maternal health care in Ethiopia

Background Globally, amidst increased utilization of facility-based maternal care services, there is continued need to better understand women’s experience of care in places of birth. Quantitative surveys may not sufficiently characterize satisfaction with maternal healthcare (MHC) in local context, limiting their interpretation and applicability. The purpose of this study is to untangle how contextual and cultural expectations shape women’s care experience and what women mean by satisfaction in two Ethiopian regions. Methods Health center and hospital childbirth care registries were used to identify and interview 41 women who had delivered a live newborn within a six-month period. We used a semi-structured interview guide informed by the Donabedian framework to elicit women’s experiences with MHC and delivery, any prior delivery experiences, and recommendations to improve MHC. We used an inductive analytical approach to compare and contrast MHC processes, experiences, and satisfaction. Results Maternal and newborn survival and safety were central to women’s descriptions of their MHC experiences. Women nearly exclusively described healthy and safe deliveries with healthy outcomes as ‘satisfactory’. The texture behind this ‘satisfaction’, however, was shaped by what mothers bring to their delivery experiences, creating expectations from events including past births, experiences with antenatal care, and social and community influences. Secondary to the absence of adverse outcomes, health provider’s interpersonal behaviors (e.g., supportive communication and behavioral demonstrations of commitment to their births) and the facility’s amenities (e.g., bathing, cleaning, water, coffee, etc) enhanced women’s experiences. Finally, at the social and community levels, we found that family support and material resources may significantly buffer against negative experiences and facilitate women’s overall satisfaction, even in the context of poor-quality facilities and limited resources. Conclusion Our findings highlight the importance of understanding contextual factors including past experiences, expectations, and social support that influence perceived quality of MHC and the agency a woman has to negotiate her care experience. Our finding that newborn and maternal survival primarily drove women’s satisfaction suggests that quantitative assessments conducted shortly following delivery may be overly influenced by these outcomes and not fully capture the complexity of women’s care experience.

Mindful unlearning in unprecedented times: Implications for management and organizations

Crises trigger both learning and unlearning at both intra-organizational and inter-organizational levels. This article stresses the need to facilitate unlearning for effective crisis management and shows how we could use mindfulness practice to enhance unlearning and transformative learning in a crisis. This study proposes the conceptualization of mindful unlearning in crisis with different mechanisms to foster unlearning in three stages of crisis (pre-crisis, during-crisis, and post-crisis). These mechanisms include mindful awareness of impermanence and sensual processing (pre-crisis stage), mindful awareness of interdependence and right intention (crisis management stage), and mindful awareness of transiency and past experiences (post-crisis stage).

Understanding Memories of the Past in the Context of Different Complex Neural Network Architectures

Deep learning (primarily using backpropagation) and neuroevolution are the preeminent methods of optimizing artificial neural networks. However, they often create black boxes that are as hard to understand as the natural brains they seek to mimic. Previous work has identified an information-theoretic tool, referred to as R, which allows us to quantify and identify mental representations in artificial cognitive systems. The use of such measures has allowed us to make previous black boxes more transparent. Here we extend R to not only identify where complex computational systems store memory about their environment but also to differentiate between different time points in the past. We show how this extended measure can identify the location of memory related to past experiences in neural networks optimized by deep learning as well as a genetic algorithm.

A cortical cell ensemble in the posterior parietal cortex controls past experience-dependent memory updating

When processing current sensory inputs, animals refer to related past experiences. Current information is then incorporated into the related neural network to update previously stored memories. However, the neuronal mechanism underlying the impact of memories of prior experiences on current learning is not well understood. Here, we found that a cellular ensemble in the posterior parietal cortex (PPC) that is activated during past experience mediates an interaction between past and current information to update memory through a PPC-anterior cingulate cortex circuit in mice. Moreover, optogenetic silencing of the PPC ensemble immediately after retrieval dissociated the interaction without affecting individual memories stored in the hippocampus and amygdala. Thus, a specific subpopulation of PPC cells represents past information and instructs downstream brain regions to update previous memories.

Bringing “Climate-Smart Forestry” Down to the Local Level—Identifying Barriers, Pathways and Indicators for Its Implementation in Practice

The theoretical concept of “climate-smart forestry” aims to integrate climate change mitigation and adaptation to maintain and enhance forests’ contributions to people and global agendas. We carried out two local transdisciplinary collaboration processes with the aim of developing local articulations of climate-smart forestry and to identify barriers, pathways and indicators to applying it in practice. During workshops in northern and southern Sweden, local stakeholders described how they would like forests to be managed, considering their past experiences, future visions and climate change. As a result, the stakeholders framed climate-smart forestry as active and diverse management towards multiple goals. They identified several conditions that could act both as barriers and pathways for its implementation in practice, such as value chains for forest products and services, local knowledge and experiences of different management alternatives, and the management of ungulates. Based on the workshop material, a total of 39 indicators for climate-smart forestry were identified, of which six were novel indicators adding to the existing literature. Our results emphasize the importance of understanding the local perspectives to promote climate-smart forestry practices across Europe. We also suggest how the concept of climate-smart forestry can be further developed, through the interplay between theory and practice.

PROJECT-BASED LEARNING: A TEACHING APPROACH WHERE LEARNING COMES ALIVE

“Doing projects” is a long-standing tradition in American education. According to Buck Institute for Education (BIE) (www.pblworks.org) the roots of PBL lie in this tradition. But the emergence of a method of teaching and learning called Project Based Learning (PBL) is the result of two important developments over the last 25 years. First, there has been a revolution in learning theory. Research in neuroscience and psychology has extended cognitive and behavioral models of learning—which support traditional direct instruction—to show that knowledge, thinking, doing, and the contexts for learning are inextricably tied. We now know that learning is partly a social activity; it takes place within the context of culture, community, and past experiences.

Adverse Childhood Experiences

Childhood trauma is as American as apple pie. The statistics are sobering. In 2018, more than 673,000 children were victims of abuse or neglect. This chapter will explore adverse childhood experiences (ACEs) in detail, relating how these past experiences could affect current student behavior. A case study will be used to illustrate the issues that teachers face in their classrooms. Research shows that trauma affects the brain and subsequently how people act and/or react. Emotional regulation, behavioral control, and cognitive processes that are affected by trauma will be explored. Further, this chapter will raise the issues of racial disproportionality in identification and labeling of behavioral disorders and recommendations for special education among students who may have been exposed to trauma. Lastly, recommendations for best practice will be outlined to support educators in the field.

An improved Random Forest based on Feature Selection and Feature weighting for case retrieval in CBR system Application to medical data

: The medical diagnostic process works very similarly to the Case Based Reasoning (CBR) cycle scheme. CBR is a problem solving approach based on the reuse of past experiences called cases. To improve the performance of the retrieval phase, a Random Forest (RF) model is proposed, in this respect we used this algorithm in three different ways (three different algorithms): Classic Random Forest (CRF) algorithm, Random Forest with Feature Selection (RF_FS) algorithm where we selected the most important attributes and deleted the less important ones and Weighted Random Forest (WRF) algorithm where we weighted the most important attributes by giving them more weight. We did this by multiplying the entropy with the weight corresponding to each attribute.We tested our three algorithms CRF, RF_FS and WRF with CBR on data from 11 medical databases and compared the results they produced. We found that WRF and RF_FS give better results than CRF. The experiemental results show the performance and robustess of the proposed approach.

A Review of Chronological Evolution of Air Quality Indexing Systems (1966 To 2021)

Air quality index (AQI) also known as air pollution index (API) is the way of describing ambient air quality to assess the health risk associated with pollution. With the advent of time, there have been several air quality indexing systems starting from the first air Quality Index developed in 1966 by Marvin H. Green and various modifications have been made ever since to improve the accuracy of measurement. Such systems can assess the air quality by several factors like the concentration of different pollutants or by various empirically established formulas based on past experiences. In this review article, an effort has been made to chronologically evaluate the AQI system developed across the world from 1966 to 2021. Every indexing system has its own unique method for air quality determination and each method has its own merits and demerits. This pape rcovers various parameters, empirical relationships, standards, merits, and demerits, which in hind sight will help to develop an amalgamation of various indexing systems that can be used as a standard method for monitoring the quality of air. This paper also covers the AQI systems that prevail in India. A fuzzy logic system is very helpful in handling the uncertainty in air quality assessment. So, fuzzy-based air quality indexing systems developed from 2010 to 2017 have also been reviewed. The review of articles established that the results obtained through fuzzybased AQI aremore reliable than the other methods. Out of all the above describing methods, fuzzy synthetic evaluation-based AQI system and fuzzy air quality health index (FAQHI) are more powerful tools to describe the air quality. But till 2017, thereis no development of AQI systems based on fuzzy logic, considering PM2.5 as one of the pollutants. So, there is a need to develop the fuzzy-based AQI system considering PM2.5 as a pollutant with other air pollutants.

Communicating With Nursing Home Residents About End of Life

Approximately 33% of the 1.2 million older individuals residing in nursing homes have the capacity to discuss their preferences for end-of-life care, and 35% will die within their first year in the nursing home. These conversations necessary to promote care consistent with the resident’s preferences are often limited and most often occur when the resident is actively dying. The purpose of this secondary analysis was to understand the resident’s perspectives on end-of-life communication in the nursing home and suggest approaches to facilitate this communication. We interviewed 46 participants (16 residents, 10 family members, and 20 staff) in a Southeast Florida nursing home from January to May 2019. The data were analyzed using descriptive and pattern coding and matrices to decipher preliminary categories and thematic interpretation within and across each participant group. Two themes emerged from this secondary analysis that residents assume others know their end-of-life preferences, and past experiences may predict future end-of-life choices. Residents and family members were willing to discuss end-of-life care. Study findings also suggested that past experiences with the end-of-life and critical illness of another could impact residents’ and family members’ end-of-life care decisions, and that nurses’ recognition of subtle signs of a resident’s decline may trigger provider-initiated end-of-life conversations. Future research should focus on strategies to promote earlier end-of-life discussions to support independent decision-making about end-of-life care in this relatively dependent population of older adults.