Scientific Publications on Nursing for COVID-19 in Patients With Cancer: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The needs of patients with cancer must be met, especially in times of crisis. The advent of the pandemic triggered a series of strategic actions by the nursing team to preserve the health of patients and professionals-hence the importance of studies on nursing care actions provided to patients with cancer during the COVID-19 pandemic. It is known that these patients are susceptible to severe COVID-19. However, no previous review has summarized the findings of scientific studies on nursing for COVID-19 in patients with cancer. OBJECTIVE: This study aims to map the topics addressed in scientific studies on nursing for COVID-19 in patients with cancer. METHODS: A scoping review was conducted using the methodology described in the Joanna Briggs Institute Reviewers' Manual 2015. The research question was elaborated using the population, concept, and context framework: What topics have been studied in nursing publications about COVID-19 in adult patients with cancer? The searches were carried out in 8 databases between April and November 2021 without time restrictions. RESULTS: In total, 973 publications were identified using the search strategies in the databases, and 12 papers were retrieved by consulting the references. A total of 31 (3.2%) publications were included in the final analysis, generating 4 thematic categories on the subject: "restructuring the services: how oncology nursing was adapted during the pandemic," "experiences of patients and performance of the nursing team during the COVID-19 pandemic," "protocols and recommendations for dealing with the COVID-19 pandemic," and "challenges and the role of oncology nurses facing the COVID-19 pandemic." CONCLUSIONS: Several strategies used by oncology nurses to face the COVID-19 pandemic in the international scenario were identified. Reports about the restructuring of services and the team's reactions to the pandemic predominated. However, there is a lack of reports regarding emotional support strategies for health care professionals. Another gap identified was the scarcity of clinical studies on the activities developed by oncology nurses. Therefore, there is a need for clinical research in the oncology area and emotional coping strategies to support oncology nurses.
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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 | 0.031 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.049 | 0.056 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".