Evaluation of online, publicly available cancer‐related educational and self‐management resources for symptom management
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to evaluate the readability, suitability, and quality of publicly available online self-management (SM) resources for people with cancer. METHODS: Resources were identified using two strategies: (1) a targeted search of 20 Canadian organizations and (2) a Google search. These were evaluated using the Suitability Assessment of Materials (SAM), the DISCERN tool for quality, and readability indices. The SM skills (e.g., problem-solving) and symptom management strategies addressed by each resource were also assessed. Descriptive and hierarchical cluster analyses were performed to identify resources of the highest suitability and quality as well as resource characteristics associated with higher quality and suitability. RESULTS: A total of 92 resources were evaluated. The mean reading grade level for English resources was 10.29 (SD = 1.64, range of 7.05 to 15.09) and 12.62 for French resources (SD = 2.27, range of 10.12 to 15.65). The mean SAM score across the sample was 50.4% (SD = 10.6%), or 'adequate', and the mean DISCERN score was 61.1% (SD = 11.8%), or 'fair'. The cluster analysis indicated that 10 resources scored highly on both the SAM and the DISCERN. In total, 91 symptom management strategies were identified. On average, resources addressed 2.73 SM skills (SD = 1.58). CONCLUSIONS: There is a need for plain language resources for people with lower reading ability and resources that incorporate more SM skills. Study findings will help healthcare professionals, patients, and their families identify optimal resources to address cancer-related symptoms.
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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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".