The Evaluation of the Suitability, Quality, and Readability of Publicly Available Online Resources for the Self-Management of Fear of Cancer Recurrence
Bibliographic record
Abstract
Cancer survivors often rely on the internet for health information, which has varying levels of readability, suitability, and quality. There is a need for high-quality online self-management resources for cancer survivors with fear of cancer recurrence (FCR). This study evaluated the readability, suitability, and quality of publicly available online FCR self-management resources. A Google search using FCR-related keywords identified freely available FCR self-management resources for cancer survivors in English. Resource readability (reading grade level), suitability, and quality were evaluated using relevant assessment tools. Descriptive statistics and cluster analysis identified resources with higher suitability and quality scores. Mean resource (n = 23) readability score was grade 11 (SD = 1.6, Range = 9–14). The mean suitability score was 56.0% (SD = 11.4%, Range = 31.0–76.3%), indicating average suitability and the mean quality score was 53% (SD = 11.7%, Range = 27–80%), indicating fair quality. A cluster of 15 (65%) resources with higher suitability and quality scores was identified. There were no significant associations between suitability or quality scores and the type of organisation that published the resources. Online FCR self-management resources varied in readability, suitability and quality. Resources with higher quality and suitability scores relative to other resources are identified for use by healthcare professionals and cancer survivors. Resources that are more culturally appropriate, with lower reading grade levels and detailed self-management strategies are needed.
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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 | 0.009 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".