An Acute Stress Scale for Health Care Professionals Caring for Patients With COVID-19: Validation Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has affected the response capacity of the health care workforce, and health care professionals have been experiencing acute stress reactions since the beginning of the pandemic. In Spain, the first wave was particularly severe among the population and health care professionals, many of whom were infected. These professionals required initial psychological supports that were gradual and in line with their conditions. OBJECTIVE: In the early days of the pandemic in Spain (March 2020), this study aimed to design and validate a scale to measure acute stress experienced by the health care workforce during the care of patients with COVID-19: the Self-applied Acute Stress Scale (EASE). METHODS: Item development, scale development, and scale evaluation were considered. Qualitative research was conducted to produce the initial pool of items, assure their legibility, and assess the validity of the content. Internal consistency was calculated using Cronbach α and McDonald ω. Confirmatory factor analysis and the Mann-Whitney-Wilcoxon test were used to assess construct validity. Linear regression was applied to assess criterion validity. Back-translation methodology was used to translate the scale into Portuguese and English. RESULTS: A total of 228 health professionals from the Spanish public health system responded to the 10 items of the EASE scale. Internal consistency was .87 (McDonald ω). Goodness-of-fit indices confirmed a two-factor structure, explaining 55% of the variance. As expected, the highest level of stress was found among professionals working in health services where a higher number of deaths from COVID-19 occurred (P<.05). CONCLUSIONS: The EASE scale was shown to have adequate metric properties regarding consistency and construct validity. The EASE scale could be used to determine the levels of acute stress among the health care workforce in order to give them proportional support according to their needs during emergency conditions, such as the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".