The Impact of Canadian Medical Delays and Preventive Measures on Breast Cancer Experience: A Silent Battle Masked by the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic led to the prioritization of breast cancer services towards patients who are currently in treatment or diagnosed with advanced stages of breast cancer, and the self-assessment of both tumor growth and treatment side effects. Alongside the stress associated with cancer itself, delays and complications due to COVID-19 may impact patients' mental health. PURPOSE: To describe the experiences of Canadians living with breast cancer who received a diagnosis and/or treatment during the pandemic, and to identify their recommendations for improving patients well-being during future pandemics. METHODS: Semi-structured interviews were conducted with eighteen women living with breast cancer who also completed the Distress Thermometer questionnaire. The transcripts were analyzed using a descriptive thematic content methodology. RESULTS: Women who started their breast cancer screening or treatment before the pandemic reported fewer delays and less psychological distress than those who started during the pandemic. Participants reported feeling dehumanized while receiving their medical care, being unable to be accompanied during medical visits, and fearing treatment interruption during the pandemic. Patient recommendations for improving care and psychological support included the presence of family caregivers at consultations to receive the diagnosis and for the first treatment session. CONCLUSION: Study findings provide new insights on how healthcare restrictions during the pandemic impacted on patient experiences and their well-being during screening and treatment for breast cancer. The need for cancer nursing practices and care delivery strategies that promote the delivery of compassionate, patient-centred care and the provision of psychological support during future pandemics are identified.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".