Impact of COVID-19 on older adults with cancer and their caregivers’ cancer treatment experiences study: The ICE-OLD study
Bibliographic record
Abstract
The COVID-19 pandemic and health services impacts related to physical distancing posed many challenges for older adults with cancer. The goal of this study was to examine the impact of the pandemic on cancer treatment plans and cancer treatment experiences of older adults (ie, aged 65 years and older) and their caregiver' experiences of caring for older adults during the pandemic to highlight gaps in care experienced. In this multi-centre qualitative study guided by an interpretive descriptive research approach we interviewed older adults diagnosed with cancer and caregivers caring for them. Participants were recruited via cancer treatment centres in the provinces of British Columbia and Ontario (Vancouver and Toronto), Canada, and through an online ad sent out through patient advocacy organization newsletters. Interviews were recorded and transcribed verbatim and data were analyzed using an interpretive thematic analysis approach. A total of 27 individuals (17 older adults, 52.9% female; 10 caregivers, 90% female) participated in interviews lasting on average 45 minutes. Older adults with cancer described many impacts and pressures created by the pandemic on their cancer experiences, though they generally felt that the pandemic did not impact treatment decisions made and access to care. We grouped our findings into two main themes with their accompanying sub-themes, related to: (1) alterations in the individual and dyadic cancer experience; and (2) navigating health and cancer systems during the pandemic. The additional stressors the pandemic placed on older adults during their treatment and decision-making process and their caregivers expose the need to create or avail additional supports for future disruptions in care.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".