Cause of death estimation from verbal autopsies: is the open response redundant or synergistic?

Civil registration and vital statistics systems capture birth and death events to compile vital statistics and to provide legal rights to citizens. Vital statistics are a key factor in promoting public health policies and the health of the population. Medical certification of cause of death is the preferred source of cause of death information. However, two thirds of all deaths worldwide are not captured in routine mortality information systems and their cause of death is unknown. Verbal autopsy is an interim solution for estimating the cause of death distribution at the population level in the absence of medical certification. A Verbal Autopsy (VA) consists of an interview with the relative or the caregiver of the deceased. The VA includes both Closed Questions (CQs) with structured answer options, and an Open Response (OR) consisting of a free narrative of the events expressed in natural language and without any pre-determined structure. There are a number of automated systems to analyze the CQs to obtain cause specific mortality fractions with limited performance. We hypothesize that the incorporation of the text provided by the OR might convey relevant information to discern the CoD. The experimental layout compares existing Computer Coding Verbal Autopsy methods such as Tariff 2.0 with other approaches well suited to the processing of structured inputs as is the case of the CQs. Next, alternative approaches based on language models are employed to analyze the OR. Finally, we propose a new method with a bi-modal input that combines the CQs and the OR. Empirical results corroborated that the CoD prediction capability of the Tariff 2.0 algorithm is outperformed by our method taking into account the valuable information conveyed by the OR. As an added value, with this work we made available the software to enable the reproducibility of the results attained with a version implemented in R to make the comparison with Tariff 2.0 evident

Factors Influencing Dietetic Interns\u27 Dietary Habits During Supervised Practice

Supervised practice is a prerequisite to becoming a registered dietitian. Research suggests that environmental and social factors may affect dietary choices. This focus group research aimed to gather opinions from dietetic interns to understand what factors related to supervised practice, if any, affected their dietary habits. Qualitative data were collected via seven recorded virtual focus groups in which trained moderators facilitated a discussion using a series of controlled questions. Participants, dietetic interns (n = 42) who were currently completing or had completed their supervised practice within the previous six months, attended one of seven virtual focus groups. Each focus group had five to eight participants. Transcripts were separately coded by two trained researchers using a grounded theory approach to identify themes and subthemes. Researchers discussed any disagreements in coding and established a consensus. Elements related to the dietetic internship were observed to influence participants’ dietary choices. Main themes included time, finances, food access and availability, physical and mental effects, non-supervised practice factors, and social factors. Dietetic programs and preceptors should explore ways to raise interns’ awareness and minimize the potential negative impacts of these factors on interns’ dietary habits to improve their overall internship experience

An Implemented Interlanguage Model for Learners of Basque

this paper we mainly show the way in which we have adapted the Natural Language Processing tools for Basque previously developed by our group for the detection of both, deviant and correct linguistic structures at word level; this is the basis for the internal representation of the student's language knowledge, that is th