Describing Supportive Care Programming Access and Comfort Gathering through the COVID-19 Pandemic: An Observational Mixed Methods Study with Adults Affected by Cancer
Bibliographic record
Abstract
Supportive care programming helps many adults affected by cancer manage concerns related to their disease. Public health restrictions imposed by the COVID-19 pandemic have undoubtedly changed the nature of supportive care programming delivery. Yet, access to supportive care programming and comfort gathering through the pandemic are unknown. As a first step towards informing ongoing supportive care programming for adults affected by cancer, this observational, mixed methods study described supportive care programming access through the COVID-19 pandemic and comfort returning to in-person supportive care programming as restrictions eased. Adults affected by cancer (n = 113; mean age = 61.9 ± 12.7 years; 68% female) completed an online survey, and descriptive statistics were computed. A purposeful sample of survey participants (n = 12; mean age = 58.0 ± 14.5 years; 58% female) was subsequently recruited to complete semi-structured interviews. Interviews were analyzed using reflexive thematic analysis. Less than half (41.6%) of the survey sample reported accessing supportive care programming during the pandemic, and of those who had accessed supportive care programming, most (65.6%) perceived similar or greater access than pre-pandemic. During interviews, participants described the ways online delivery enhanced their access and reduced barriers to supportive care programming. However, physical activity programming was described as challenging to navigate online. With restrictions easing, most of the survey sample (56.6%) reported being apprehensive about returning to in-person supportive care programming and identified the protocols that would make them feel safe to gather. During interviews, participants recounted struggling to balance their need for social connection with their health and safety. This study provides evidence to inform supportive care programming for adults affected by cancer through the COVID-19 pandemic. Findings suggest online delivery can enhance access to some types of supportive care programming for some adults affected by cancer, and that efforts are needed to ensure all adults affected by cancer feel comfortable gathering in-person.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".