The Effect of COVID-19 on the Mental Health of Breast Cancer Patients Undergoing Treatment: A Scoping Review
Bibliographic record
Abstract
Introduction: Patients with breast cancer experienced difficulties making appointments, treatment delays, and delayed screening during the COVID-19 pandemic. As a result of these challenges for individuals with breast cancer diagnoses, it is believed that the COVID-19 pandemic had a detrimental effect on these patients’ mental health. The purpose of this review was to examine the effect of COVID-19 on the mental health of breast cancer patients undergoing treatment through a scoping review. Methods: The methods were guided by the Arksey and O’Malley framework, described in the Joanna Briggs Institute guidelines, and the reporting is compliant with PRISMA-ScR Checklist. Searches were conducted in the databases Medline via OVID and CINAHL from February 2023 to March 2023, with studies being published only from January 2020 to present. Inclusion criteria were breast cancer patients receiving breast cancer treatment such as surgery or chemotherapy, observational studies such as cross-sectional, and studies that measured the impact on mental health during the COVID-19 pandemic. Data were then extracted using a charting form. Results: A total of 21 studies were included. Either depression, anxiety or stress or a combination of them were examined in all the studies that were included. Thirteen of the studies included used the Hospital Anxiety Depression Scale (HADS) and/or the Generalized Anxiety Disorder (GAD-7) for their measures. Seventeen studies showed that while undergoing active treatment during COVID-19, there were increased levels of depression, anxiety, and stress. Four studies that were included did not see a difference in these mental health outcomes during the pandemic. Discussion: Many studies stated the importance of how mental health interventions can be key to preventing higher rates of depression and anxiety. As such, the mental health of breast cancer patients must be prioritized by introducing changes to health care distribution and through providing psychological interventions to these patients. Conclusion: This scoping review demonstrated that breast cancer patients had increased rates of depression, anxiety, stress, and other mental health outcomes during the pandemic. Future research should be conducted to examine the effects on psychological interventions that focus on improving mental health in breast cancer patients.
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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 | 0.015 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".