Cancer Survivors’ Evolving Perceptions of a New Supportive Virtual Program
Bibliographic record
Abstract
This qualitative study begins to explore cancer survivors' evolving perceptions of "Focus on the Future," a 6-week supportive virtual program led by trained volunteers and health care professionals. Through purposive sampling, participants (n = 10) enrolled in the program were individually interviewed shortly before attending, mid-way through, and at program completion. Interviews were digitally recorded and transcribed verbatim. Thematic analysis was used to develop key elements of program expectations and users' perceptions over time. Three themes transpired from the data: (1) Trustworthiness and timeliness of survivorship information and expert guidance, (2) Normalization of survivors' experiences, and (3) Virtual program delivery issues. Some participants' perceptions remained unchanged from pre-program expectations to post-program completion such as appreciating the efficiency of virtual delivery and "health safe" exchanges given the COVID-19 pandemic. In contrast, other perceptions became more polarized including drawbacks related to "more superficial" virtual connections and uneven topic relevance as the program evolved. Program participants appreciated timely information and support from volunteers and experts through virtual means and consecutive weekly sessions. Gauging participants' perceptions across time also offer opportunities to adjust program content and delivery features. Future research should explore key program development strategies to ensure that cancer supportive programs are optimally person-centered, co-designed, and situation-responsive.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 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".