Coping during COVID-19: a mixed methods study of older cancer survivors
Bibliographic record
Abstract
PURPOSE: Older cancer survivors are among the most vulnerable to the negative effects of COVID-19 and may need specific survivorship supports that are unavailable/restricted during the pandemic. The objective of this study was to explore how older adults (≥ 60 years) who were recently (≤ 12 months) discharged from the care of their cancer team were coping during the pandemic. METHODS: We used a convergent mixed method design (QUAL+quan). Quantitative data were collected using the Brief-COPE questionnaire. Qualitative data were collected using telephone interviews to explore experiences and strategies for coping with cancer-related concerns. RESULTS: The mean sample age (n = 30) was 72.1 years (SD 5.8, range 63-83) of whom 57% identified as female. Participants' Brief-COPE responses indicated that they commonly used acceptance (n = 29, 96.7%), self-distraction (n = 28, 93.3%), and taking action (n = 28, 93.3%) coping strategies. Through our descriptive thematic analysis, we identified three themes: (1) drawing on lived experiences, (2) redeploying coping strategies, and (3) complications of cancer survivorship in a pandemic. Participants' coping strategies were rooted in experiences with cancer, other illnesses, life, and work. Using these strategies during the pandemic was not new-they were redeployed and repurposed-although using them during the pandemic was sometimes complicated. These data were converged to maximize interpretation of the findings. CONCLUSIONS: Study findings may inform the development or enhancement of cancer and non-cancer resources to support coping, particularly using remote delivery methods within and beyond the pandemic. Clinicians can engage a strengths-based approach to support older cancer survivors as they draw from their experiences, which contain a repository of potential coping skills.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".