An evaluation of a cancer survivorship education class for follow-up care
Bibliographic record
Abstract
The Wellness Beyond Cancer Program is part of a tertiary care hospital in Ontario, Canada. It provides cancer survivors with information and resources needed to self-manage their follow-up care (i.e., learn relevant information and skills to adapt to life with a chronic illness) after active cancer treatment (i.e., chemotherapy, radiation). A program evaluation was conducted on the two-hour survivorship education class (one component of the overall Wellness Beyond Cancer Program) with the purpose of evaluating whether attendance increased survivors’ perceived knowledge and intent to self-manage follow-up care. Breast (n = 107) and colorectal (n = 38) cancer survivors who attended classes completed questionnaires on information needs and intent to self-manage pre- and postclass. Perceived increase in knowledge and intent to self-manage follow-up care were unrelated to age, gender, or time since diagnosis. After attending the class, survivors reported increased knowledge (F(1,11) = 144.6, p < .001) and intent to participate in self-management of their follow-up care (F(1,103) = 57.3, p < .001). Improvements in knowledge predicted increased intent to self-manage (R2 = .64; F(4,86) = 38.5, p < .001). Colorectal cancer survivors showed greater improvement in intent to self-manage than breast cancer survivors (β = .14, t = 2.2, p < .05). These results can inform the development and implementation of future education classes for survivors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".