Psychological Distress and Unmanaged Negative Emotions: Examining Resilience Among Nurses Working on COVID-19 Designated Inpatient Units
Bibliographic record
Abstract
Anecdotal evidence suggests nurses are engaging in resilience-based strategies to mitigate increased levels of psychological distress and unmanaged negative emotions they have been experiencing. Nurses' levels of resilience during the coronavirus disease 2019 (COVID-19) pandemic have not been clearly articulated, specifically in relation to psychological distress and negative emotions. The purpose of the current mixed-methods non-experimental descriptive study was to examine nurses' resilience during the pandemic. Sixty RNs working in acute care hospitals on inpatient units designated to care for patients with COVID-19 completed the study survey and 20 of these RNs completed an interview. Findings indicate moderate levels of resilience among participants, with the need to increase resources and support emerging as a common theme among the qualitative data. Suggestions for integration of resilience-based strategies into the clinical setting, such as creation of a dedicated space for nurses to engage in mindfulness, relaxation, and meditation, were put forward. [ Journal of Psychosocial Nursing and Mental Health Services, 60 (9), 24–28.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".