Conectar
To examine symptoms of depression and generalised anxiety among nurses over 2 years during the pandemic and compare them to the general population.
The COVID-19 pandemic has led to a significant increase in mental stress among the population worldwide. Nursing staff have been identified as being under remarkable strain.
A multicentre prospective longitudinal study.
Symptoms of depression and generalised anxiety in 507 nurses were examined at four different time points (T1: April–July 2020, T2: November 2020–January 2021, T3: May–July 2021, T4: February–May 2022). Results were compared with values of the German general population, presence of gender-specific differences was analysed and frequencies of clinically relevant levels of depression and anxiety were determined.
Throughout the study (T1–T4), a significant increase in depressive and anxiety symptoms was observed. At all four measurement time points, nurses showed significantly higher prevalence for depression and anxiety compared to the German general population. No significant gender differences were found. Frequencies for probable depression and generalised anxiety disorder among nurses were: 21.6% and 18.5% (T1), 31.4% and 29.2% (T2), 29.5% and 26.2% (T3), 33.7% and 26.4% (T4).
During the pandemic, symptoms of depression and generalised anxiety among nurses increased significantly and remained elevated. Their symptom levels were permanently higher than in the general population. These findings strongly suggest that the circumstances of the pandemic severely affected nurses´ mental health.
The COVID-19 pandemic caused a great mental strain on caregivers. This study was able to demonstrate the significant increase in depression and anxiety among nurses during the pandemic. It highlights the urgent need for prevention, screening and support systems in hospitals.
Supportive programmes and preventive services should be developed, not least to prevent the growing shortage of nurses in the health care systems.
The study adhered to relevant EQUATOR guidelines. The STROBE checklist for cohort study was used as the reporting method.
Five hundred and seven nurses completed the questionnaire and provided data for analysis.
The study was registered with the German Clinical Trials Register (https://drks.de/search/en) under the following ID: DRKS00021268.
To explore whether large language models (LLMs) Generated Pre-trained Transformer (GPT)-3 and ChatGPT can write clinical letters and predict management plans for common orthopaedic scenarios.
Fifteen scenarios were generated and ChatGPT and GPT-3 prompted to write clinical letters and separately generate management plans for identical scenarios with plans removed.
Letters were assessed for readability using the Readable Tool. Accuracy of letters and management plans were assessed by three independent orthopaedic surgery clinicians.
Both models generated complete letters for all scenarios after single prompting. Readability was compared using Flesch-Kincade Grade Level (ChatGPT: 8.77 (SD 0.918); GPT-3: 8.47 (SD 0.982)), Flesch Readability Ease (ChatGPT: 58.2 (SD 4.00); GPT-3: 59.3 (SD 6.98)), Simple Measure of Gobbledygook (SMOG) Index (ChatGPT: 11.6 (SD 0.755); GPT-3: 11.4 (SD 1.01)), and reach (ChatGPT: 81.2%; GPT-3: 80.3%). ChatGPT produced more accurate letters (8.7/10 (SD 0.60) vs 7.3/10 (SD 1.41), p=0.024) and management plans (7.9/10 (SD 0.63) vs 6.8/10 (SD 1.06), p
This study shows that LLMs are effective for generation of clinical letters. With little prompting, they are readable and mostly accurate. However, they are not consistent, and include inappropriate omissions or insertions. Furthermore, management plans produced by LLMs are generic but often accurate. In the future, a healthcare specific language model trained on accurate and secure data could provide an excellent tool for increasing the efficiency of clinicians through summarisation of large volumes of data into a single clinical letter.
by Caterina Casalini, Yema D’Almeida, Moussa Ariziki Nassam, Essopha Kokoloko, Souley Wade, Jean Paul Tchupo, Messan Damarly, Justin Mandala, Michele Lanham, Natasha Mack, Chris Akolo, Vincent Polakinam Pitche, Hugues Guidigbi, Claver Anoumou Dagnra
BackgroundAccording to UNAIDS, Togo halved AIDS-related deaths among children ages 0–14 from 2010 to 2020. However, available data show low dolutegravir (DTG)-containing antiretroviral therapy (ART) coverage and low viral load suppression (VLS) among children living with HIV (CLHIV). We analyzed routine facility data before and after implementation of root-cause-based solutions for improving DTG coverage, viral load (VL) testing coverage, and VLS among CLHIV.
DescriptionWe analyzed routine data for CLHIV ≤14 years from October 2019 through September 2022. We assessed proportion of CLHIV on ART receiving DTG, VL testing coverage (CLHIV on ART with documented VL test result), and VLS (CLHIV with documented VL test result of Results
From baseline (October 2019–September 2020) to endline (October 2021–September 2022), increases were observed for DTG coverage (52% to 71%), VL testing coverage (48% to 90%), and VLS (64% to 82%). Age-disaggregated data showed positive trends.
ConclusionsRoot-cause-based solutions and granular data use increased DTG coverage, resulting in increased VL testing and VLS among CLHIV. These interventions should be scaled and become the national standard of care.
FreshRSS 