Help-seeking for 'memory loss' by older adults in India : patient, caregiver and health providers' perspectives

ABSTRACT: Background. In India, prevalence rates of dementia and prodromal amnestic Mild Cognitive Impairment (MCI) are 3.1% and 4.3% respectively. Most Indians refer to the full spectrum of cognitive disorders simply as ‘memory loss.’ Barring prevention or cure, these conditions will rise rapidly with population aging. Evidence-based policies and practices can improve the lives of affected individuals and their caregivers, but will require timely and sustained uptake. Objectives. Framed by social cognitive theories of health behavior, this study explores the knowledge, attitudes and practices concerning cognitive impairment and related service use by older adults who screen positive for MCI, their primary caregivers, and health providers. Methods. I used the Montreal Cognitive Assessment to screen for cognitive impairment in memory camps in Mumbai. To achieve sampling diversity, I used maximum variation sampling. Ten adults aged 60+ who had no significant functional impairment but screened positive for MCI and their caregivers participated in separate focus groups. Four other such dyads and six doctors/ traditional healers completed in-depth interviews. Data were translated from Hindi or Marathi to English and analyzed in Atlas.ti using Framework Analysis. Findings. Knowledge and awareness of cognitive impairment and available resources were very low. Physicians attributed the condition to disease-induced pathology while lay persons blamed brain malfunction due to normal aging. Main attitudes were that this condition is not a disease, is not serious and/or is not treatable, and that it evokes stigma toward and among impaired persons, their families and providers. Low knowledge and poor attitudes impeded help-seeking. Conclusions. Cognitive disorders of aging will take a heavy toll on private lives and public resources in developing countries. Early detection, accurate diagnosis, systematic monitoring and quality care are needed to compress the period of morbidity and promote quality of life. Key stakeholders provide essential insights into how scientific and indigenous knowledge and sociocultural attitudes affect use and provision of resources.

Narrative Memory in Hyperfiction and Games

The aim of this paper is to address some theoretical issues concerning the narrative practice in cyberspace. From a narratological perspective it intends to clarify the functioning of time and space in storytelling. For that purpose it traces the concept(s) of memory inherited from rhetoric; the use of memory as a narrative device in traditional accounts; the adaptations imposed by hyperfiction. Using practical examples (including two Portuguese case studies - InStory 2006, and Noon 2007) it will show how narrative memory strategies can be helpful in game literacy. The main purpose is to contribute to serious game research and (trans)literary studies.

Scaling In-Memory databases on multicores

Current computer systems have evolved from featuring only a single processing unit and limited RAM, in the order of kilobytes or few megabytes, to include several multicore processors, o↵ering in the order of several tens of concurrent execution contexts, and have main memory in the order of several tens to hundreds of gigabytes. This allows to keep all data of many applications in the main memory, leading to the development of inmemory databases. Compared to disk-backed databases, in-memory databases (IMDBs) are expected to provide better performance by incurring in less I/O overhead. In this dissertation, we present a scalability study of two general purpose IMDBs on multicore systems. The results show that current general purpose IMDBs do not scale on multicores, due to contention among threads running concurrent transactions. In this work, we explore di↵erent direction to overcome the scalability issues of IMDBs in multicores, while enforcing strong isolation semantics. First, we present a solution that requires no modification to either database systems or to the applications, called MacroDB. MacroDB replicates the database among several engines, using a master-slave replication scheme, where update transactions execute on the master, while read-only transactions execute on slaves. This reduces contention, allowing MacroDB to o↵er scalable performance under read-only workloads, while updateintensive workloads su↵er from performance loss, when compared to the standalone engine. Second, we delve into the database engine and identify the concurrency control mechanism used by the storage sub-component as a scalability bottleneck. We then propose a new locking scheme that allows the removal of such mechanisms from the storage sub-component. This modification o↵ers performance improvement under all workloads, when compared to the standalone engine, while scalability is limited to read-only workloads. Next we addressed the scalability limitations for update-intensive workloads, and propose the reduction of locking granularity from the table level to the attribute level. This further improved performance for intensive and moderate update workloads, at a slight cost for read-only workloads. Scalability is limited to intensive-read and read-only workloads. Finally, we investigate the impact applications have on the performance of database systems, by studying how operation order inside transactions influences the database performance. We then propose a Read before Write (RbW) interaction pattern, under which transaction perform all read operations before executing write operations. The RbW pattern allowed TPC-C to achieve scalable performance on our modified engine for all workloads. Additionally, the RbW pattern allowed our modified engine to achieve scalable performance on multicores, almost up to the total number of cores, while enforcing strong isolation.