Responding to Health Care Professionals' Mental Health Needs During COVID-19 Through the Rapid Implementation of Project ECHO
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic can cause significant mental health distress among health care professionals (HCPs). We describe the psychological needs of HCPs during COVID-19 and the implementation of Project Extension for Community Healthcare Outcomes (ECHO) Coping with COVID (ECHO-CWC) to help HCPs manage COVID-19 distress. METHODS: We used an established rapid implementation approach to accelerate the development and delivery of ECHO-CWC to address the emerging needs of HCPs. Participants' needs were identified using a 10-question survey of participants' perceived risk of COVID-19 and a five-item self-efficacy measure. Implementation outcomes consisted of participant engagement and session satisfaction scores using a five-point Likert scale. RESULTS: A total of 426 participants registered for ECHO-CWC. Needs assessment data (n = 129) showed that most participants reported feeling increased stress at work (84.5%), fear of infecting others (75.2%), and fear of falling ill (70.5%) from COVID-19, yet most participants accepted the risk associated with work during this time (59.7%). Participants were highly satisfied with the initial five sessions (mean = 4.26). DISCUSSION: HCPs reported the greatest concern with fears of infection and infecting others during the acute phase of the pandemic. Using an iterative curriculum design approach and existing implementation frameworks, the ECHO tele-education model can be rapidly mobilized to address HCPs' mental health needs during the COVID-19 pandemic.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".