The impact of online programming on cancer patients
Bibliographic record
Abstract
In this study, a qualitative design was used to examine how online cancer support programming has impacted cancer patients, specifically, the effects on their physical, mental, emotional, and spiritual well-being. We also investigated how cancer support programming could be improved from the perspectives of the participants. Due to the recent COVID-19 pandemic, many organizations had to shift to an online-based approach to allow for continued access for their members. The sudden shift of moving into online programming caused a significant learning curve for organization staff, volunteers, and members. In this research we asked members how they benefited from online programming and to indicate which aspects of programming they would like to see continued post-pandemic. This study was conducted entirely online using thematic analysis to analyze responses. To facilitate this study, we recruited six participants from a cancer support organization. Once participants were recruited, they received an informed consent form, interview questions, and a consent to use data form through google forms. Participants did not have access to the interview questions until they consented to participate in the study. The interview consisted of 10 questions and took approximately 30 to 45 minutes to complete. At the end of the study, participants received a consent to use data form. Through our thematic analysis, we found five themes that reflected our participants’ experiences with online programming: social connection, positive emotions, growth and gains, challenges and difficulties, and easy accessibility. Our findings showed more positive than negative outcomes through online programming, however, there were still challenges.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".