“The little lights in this dark tunnel”: Emotional support of nurses working in COVID-19 acute care hospital environments
Bibliographic record
Abstract
Background: Working on the frontlines of hospitals during the COVID-19 pandemic has been challenging and distressing for nurses. The troublesome nature of these emotions have surfaced because of uncharted territory related to this virus, compromised work conditions, unfavourable patient outcomes, and the witnessing of suffering and loss. Although there has been renewed emphasis on how to emotionally support nurses, the nature of support needed is somewhat unknown considering that healthcare professionals have not experienced a pandemic of this magnitude in their lifetime. Aim: We explored how nurses were emotionally supported and how they can be better supported while working in COVID-19 acute care hospital environments. Methods: In this narrative study, semi-structured interviews were conducted with 20 registered nurses working in hospitals in the Greater Toronto Area and working on units caring for COVID-19+ patients. Results and Conclusions: Our findings reflected the organic emergence of support, intentional forms of support, and the social justice nature of support. It is important for hospital and government leaders to employ a multifold approach to emotionally support nurses. These supports include information transparency, visible presence of leadership, and recognition of nurses’ contributions. While emotionally supporting nurses, these types of resources can act as “little lights in this dark tunnel” of COVID-19 and illuminate a path forward. Implications: Some strategies relevant to clinical practice include regular rounding of units by leaders, and transparent communication about information and resources. Other strategies are on-site psychological support and legitimate support of mental health sick days as well as lobbying governments for financial compensation for the risky work involved in being a frontline provider and appropriate provision of personal protective equipment.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".