地域の防災力を引き出す保健師の役割

我々は、A市B地区の防災力を高める取り組みとして、地域住民の防災に対するニーズ調査や災害対策委員や町内代表者との意見交換をもとに、防災講習会を共同で企画・実施した。上記の取り組みのうちニーズ調査結果と防災講習会の評価結果をもとに地域の防災力を引き出す保健師の役割について分析した。その結果、保健師の役割は、①日頃の地域保健活動を通して地域の特性や自主防災力を把握し、地域力として活かす活動を行う、②住民の自助・共助をさらに高める働きかけを行う、③個人・家族の実践力や町内全体の防災力を高める活動を支援することであるといえた

Development and external validation of a deep learning-based computed tomography classification system for COVID-19

[BACKGROUND] We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR). [METHODS] We used 2, 928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2, 295 non-COVID-19 cases were included in the study. We randomly divided cases into training and tuning sets at a ratio of 8:2. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR. [RESULTS] In external validation, the sensitivity and specificity of the model were 0.869 and 0.432, at the low-level cutoff, 0.724 and 0.721, at the high-level cutoff. Area under the receiver operating characteristic was 0.76. [CONCLUSIONS] Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner at emergency departments. Further studies are warranted to improve model specificity