Leading through COVID-19: understanding and supporting grief and loss
Bibliographic record
Abstract
The COVID-19 pandemic has created an environment in which grief and loss are being experienced collectively. This grief can lead to increased burnout, decreased productivity, and increased likelihood of job turnover. With health care workers already facing increased risks because of their frontline pandemic responsibilities, it is important to provide leaders with knowledge and tools to support their grieving team members. Understanding the Kübler-Ross grief model, as well as grief-related concepts such as anticipatory grief, disenfranchised grief, moral injury, and complicated grief, will help leaders provide normalizing support. This approach may include building and fostering trusting relationships, engaging in self-reflection, participating in supportive conversations, and, when appropriate, sharing information around grief-support resources. There is no universal timeline for the resolution of grief; mental health impacts can last for many months and can continue to resurface for years. During the COVID-19 pandemic, we educated health care workers around the issues of grief and loss by focusing on the relationship side of the Wheel of Change, interviewing people with expertise in the area, holding town hall meetings, and hosting online “coffee and chat” sessions for physicians. We recommend relying less on policy development and, instead, focus on strengthening workplace relationships and creating opportunities for connection and discussions.
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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 | 0.001 |
| 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 | 0.001 |
| 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".