Abstract citation ID: ckac129.639 Improving the quality of translation for patients with language barriers

citation ID: ckac129.639 Improving the quality of translation for patients with language barriers

Background: About 35% of adult Europeans remain not fully vaccinated against COVID-19.Health literacy (HL), health consciousness (HC), and health locus of control (HLC) have been linked to different health behaviors, yet their role in COVID-19 vaccination uptake remains unclear.Here, we report preliminary findings from a cross-sectional survey conducted in Greece and Cyprus, aiming to elucidate the aforementioned associations.Methods: Participant recruitment for the current analysis took place from January to June 2022, following proportional quota sampling.The current sample comprises 190 participants, with full information on COVID-19 vaccine hesitancy (composite scale outcome) and refusal (binary outcome), as well as HL (European Health Literacy Survey Questionnaire-Q16), HC (Health Consciousness Scale-G), and HLC (Multidimensional Health Locus of Control Form B).Linear and logistic regression analyses were used to determine associations between the aforementioned factors, using the standardized versions of the independent variables.

Conclusions:
Health literacy and locus of control and to a lesser extent health consciousness, are independent predictors of COVID-19 vaccine hesitancy and refusal.Increasing vaccination uptake Issue/problem: For some migrants, specific health information tools are needed in order to counterbalance lack of access to information both in countries of origin and of destination.Description of the problem: Migrants originating from Pakistan and newly arrived in France are highly affected by for instance hepatitis C.Many of them have a low level of litteracy, including health litteracy.We decided to create a bilingual Urdu/French website, to provide information on hepatitis C, but also on sexual and mental health.The project was conceptualized in a participary manner, in order to fit the targeted population needs.The project started in June 2021 and the website is expected to be launched by September 2022.We report here some of the key challenges which emerged when working with such hard-toreach community.

Results:
The participatory approach involving community members consisted of 3 steps.In step 1, we confirmed content needs, and assessed preferences in terms of type of media to be used and media styles.About 30 themes were prioritized and the preferred media was video with formal style.After writing scripts, we organized focus-groups discussions to create culturally-appropriate messages.A third and last step will be the selection of titles and keywords for internet search, after shooting videos.Lessons: Despite working closely with a Pakistani cultural association and urdu-speaking health professionnals, recruitment of community members for participating in the website design happened to be extremely difficult.Despite initial enthusiasm, interests in the project tended to decrease according to sometimes hidden motivations.Focus groups dynamics were at times affected by significant differences in social situations between participants.Despite these challenges, the participatory process allowed us to reshape part of the content according to the community's communications codes, in order to enhance the efficiency of the messages.

Key messages:
Working with socially disadvantaged community members is crucial in order to reduce social inequalities in health.However, caution is needed to anticipate and decrease unfulfilled expectations.
Abstract citation ID: ckac129.642A novel stream of collaborative National portals to enhance preparedness and informed choices

Background:
The COVID-19 pandemic highlighted the importance of rapidly updating scientific information.However, the guidelines' drafting process is highly time-and resource-consuming.
The COKE Project aims to accelerate and streamline the extraction and synthesis of scientific evidence.To do so, the Project used deep learning to implement a semi-automated system that enhances the systematic literature review processes.We aim to show some preliminary results on the automatic classification of abstract sentences in papers related to COVID-19.

Methods:
The tool is based on Natural Language Processing algorithms to detect and classify PICO elements and medical terms and organize abstracts accordingly.We built a BERT + bi-LSTM language model.The tool was trained on a corpus of 24,668 abstracts unrelated to COVID-19.We assessed the tool performance in a specific topic related to COVID-19 that has not been covered during training.To carry out manual validation, we randomly selected 50 abstracts.Abstract sentences were classified by 2 domain experts into 7 types: Aim (A), Participants (P), Intervention (I), Outcome (O), Method (M), Results (R), and Conclusion (C).The performance of the tool was compared with that of the experts in terms of precision, recall, and F1.
15th European Public Health Conference 2022