Cancer Care Team Functioning during COVID-19: A Narrative Literature Review and Synthesis
Bibliographic record
Abstract
Amid pandemics, health care teams face unprecedented challenges, requiring significant efforts to sustain optimal functioning and navigate rapid practice changes. It is therefore crucial to identify factors affecting team functioning in these contexts. The present narrative review more specifically summarizes the literature on key elements of cancer teams' functioning during COVID-19. The search strategy involved four main databases (i.e., Medline OVID, EMBASE, PsycINFO, and CINAHL), as well as Google Scholar, from January 2000 to September 2022. Twenty-three publications were found to be relevant. Each was read thoroughly, and its content summarized. Across publications, three key themes emerged: (1) swiftly adopting virtual technology for communication and interprofessional collaboration, (2) promoting team resilience, and (3) encouraging self-care and optimizing team support. Our findings underscore key team functioning elements to address in future pandemics. More research is needed to document the perspectives of broader-based team members (such as patients and lay carers) to inform more comprehensive evidence-based team functioning guidelines.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".