14 research outputs found
Thinking rural health in Santal communities of West Bengal: an interprofessional bottom-up approach
BACKGROUND: An interprofessional and cross-cultural pedagogical project in community health for students in nursing, social work, anthropology and medicine at the end of the bachelor’s degree begun in 2014. After a rural context fieldwork in several Santal villages of West Bengal (India), students had to conduct a research project, based on a community-health topic. AIMS: This paper describes how such a pedagogical project, introducing students to ethnographic research, can initiate new ways of thinking for possible future health interventions in rural communities. METHODS: An inductive approach based on ethnography was used during the fieldwork, including observations, interviews, focus groups and local documentation. RESULTS: Our observations led to the finding that actions in rural health cannot be initiated without: promoting an interprofessional/interdisciplinary perspective and a culture of complexity and reflectivity; considering local populations in transition and not in a fixed homogenous situation; understanding more than imposing; taking into account local disease classification and local pragmatic solutions; considering the dialogue between bio-medicine and therapeutic pluralism; considering local perceptions and practices; considering care itineraries/pathways; and finally being conscious of our apostolic function. CONCLUSION: Our interprofessional pedagogical project promotes a bottom-up approach in dialogue with a global health vision
Recommended from our members
High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning.
We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of [Formula: see text] 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics