An accumulation of distress: Grief, loss, and isolation among healthcare providers during the COVID-19 pandemic
Bibliographic record
Abstract
This article draws on the journal entries of 62 healthcare professionals (HCP) in the United States and Canada who participated in the Pandemic Journaling Project (PJP) during 2020-2021. The HCP in this article represented healthcare fields including medicine, nursing, physical therapy, social work, and clinical psychology. In their journal entries, HCP provided accounts of witnessing the death and bereavement of their patients and loved ones; experiencing their own loss of loved ones and important milestones; facing isolation from their networks and places of meaning; and juggling increasing workloads and caregiving activities. I illustrate how these four areas were impacted by guilt, duty, ethical deliberations, and gender disparities. I argue that HCP face an accumulation of distress when they witness grief and face loss without space to process these experiences.
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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 | 0.002 | 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 | 0.007 | 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".