Preventing and Addressing the Stress Reactions of Health Care Workers Caring for Patients With COVID-19: Development of a Digital Platform (Be + Against COVID)
Bibliographic record
Abstract
BACKGROUND: COVID-19 became a major public health concern in March 2020. Due to the high rate of hospitalizations for COVID-19 in a short time, health care workers and other involved staff are subjected to a large workload and high emotional distress. OBJECTIVE: The objective of this study is to develop a digital tool to provide support resources that might prevent and consider acute stress reactions in health care workers and other support staff due to the COVID-19 pandemic. METHODS: The contents of the digital platform were created through an evidence-based review and consensus conference. The website was built using the Google Blogger tool. The Android version of the app was developed in the Java and XML languages using Android Studio version 3.6, and the iOS version was developed in the Swift language using Xcode version 11.5. The app was evaluated externally by the Andalusian Agency for Healthcare Quality. RESULTS: We detected the needs and pressing situations of frontline health care workers, and then, we proposed a serial of recommendations and support resources to address them. These resources were redesigned using the feedback received. A website in three different languages (Spanish, English, and Portuguese) and a mobile app were developed with these contents, and the AppSaludable Quality Seal was granted to the app. A specific self-report scale to measure acute stress and additional tools were included to support the health care workforce. This instrument has been used in several Latin American countries and has been adapted considering cultural differences. The resources section of the website was the most visited with 18,516 out of 68,913 (26.9%) visits, and the "Self-Report Acute Stress Scale" was the most visited resource with 6468 out of 18,516 (34.9%) visits. CONCLUSIONS: The Be + against COVID platform (website and app) was developed and launched to offer a pool of recommendations and support resources, which were specifically designed to protect the psychological well-being and the work morale of health care workers. This is an original initiative different from the usual psychological assistance hotlines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".