Practical strategies and the need for psychological support: recommendations from nurses working in hospitals during the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE: Nurses working during the coronavirus disease 2019 (COVID-19) pandemic have reported elevated levels of anxiety, burnout and sleep disruption. Hospital administrators are in a unique position to mitigate or exacerbate stressful working conditions. The goal of this study was to capture the recommendations of nurses providing frontline care during the pandemic. DESIGN/METHODOLOGY/APPROACH: Semi-structured interviews were conducted during the first wave of the COVID-19 pandemic, with 36 nurses living in Canada and working in Canada or the United States. FINDINGS: The following recommendations were identified from reflexive thematic analysis of interview transcripts: (1) The nurses emphasized the need for a leadership style that embodied visibility, availability and careful planning. (2) Information overload contributed to stress, and participants appealed for clear, consistent and transparent communication. (3) A more resilient healthcare supply chain was required to safeguard the distribution of equipment, supplies and medications. (4) Clear communication of policies related to sick leave, pay equity and workload was necessary. (5) Equity should be considered, particularly with regard to redeployment. (6) Nurses wanted psychological support offered by trusted providers, managers and peers. PRACTICAL IMPLICATIONS: Over-reliance on employee assistance programmes and other individualized approaches to virtual care were not well-received. An integrative systems-based approach is needed to address the multifaceted mental health outcomes and reduce the deleterious impact of the COVID-19 pandemic on the nursing workforce. ORIGINALITY/VALUE: Results of this study capture the recommendations made by nurses during in-depth interviews conducted early in the COVID-19 pandemic.
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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 | 0.000 |
| 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) | 0.000 | 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".