Initial findings on RESTORE for healthcare workers: an internet-delivered intervention for COVID-19-related mental health symptoms
Bibliographic record
Abstract
Many healthcare workers on the frontlines of the COVID-19 pandemic are experiencing clinical levels of mental health symptoms. Evidence-based interventions to address these symptoms are urgently needed. RESTORE (Recovering from Extreme Stressors Through Online Resources and E-health) is an online guided transdiagnostic intervention including cognitive-behavioral interventions. It was specifically designed to improve symptoms of anxiety, depression, and posttraumatic stress disorder (PTSD) associated with COVID-19-related traumatic and extreme stressors. The aims of the present study were to assess the feasibility, acceptability, and initial efficacy of RESTORE in healthcare workers on the frontline of the COVID-19 pandemic. We conducted an initial uncontrolled trial of RESTORE in 21 healthcare workers who were exposed to COVID-19-related traumatic or extremely stressful experiences in the context of their work and who screened positive for clinical levels of anxiety, depression, and/or PTSD symptoms. RESTORE was found to be feasible and safe, and led to statistically significant and large effect size improvements in anxiety, depression, and PTSD symptoms over the course of the intervention through follow-up. RESTORE has the potential to become a widely disseminable evidence-based intervention to address mental health symptoms associated with mass traumas.Clinical Trials Registration: This trial was registered with ClinicalTrials.gov ID: NCT04873622.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".