Healthcare provider experiences during COVID-19 redeployment
Bibliographic record
Abstract
PURPOSE OF REVIEW: Among the myriad traumatic impacts of COVID-19, the need for redeployment served as a significant stressor for healthcare providers (HCPs). This narrative review summarizes the current literature on HCP redeployment experiences and institutional support for staff, while proposing a theoretical approach to mitigating the negative impact on HCP mental health. RECENT FINDINGS: Redeployment was a strong predictor of negative emotions in HCP during the initial stage of the COVID-19 pandemic, whereas reflections on benefit-finding associated with redeployment were reported more frequently during later stages. In institutions where attention to redeployment impact was addressed and effective strategies put in place, redeployed HCP felt they received adequate training and support and felt satisfied with the information provided. Redeployment had the potential to yield personal feelings of accomplishment, situational leadership, meaning, and increased sense of team connectedness. SUMMARY: Benefit-finding, or posttraumatic growth, is a concept in cancer psychiatry which speaks to construing benefits from adversity to support resilience. Redeployment experiences can result in unexpected benefit-finding for individual HCPs. Taking a benefit-finding, relational, and existentially informed approach to COVID-19 redeployment might serve as an opportunity for posttraumatic growth for both individuals and institutions.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".