Evaluation of the accuracy and availability of cancer-related physical activity and sedentary behaviour information on english-language websites
Bibliographic record
Abstract
Purpose: To assess the quality and accuracy of cancer-related physical activity (PA) and sedentary behaviour (SB) information provided on reputable cancer websites from English speaking countries. Design: Cross-sectional. Sample: Reputable cancer websites from English speaking countries. Methods: A list of reputable cancer websites (N = 11) was generated from countries that speak English primarily (e.g., Canada, Australia). These websites were assessed for quality and accuracy based on a detailed coding framework (e.g., PA guidelines, PA and cancer prevention). Frequencies and descriptive statistics were derived for website characteristics of interest. Findings: Websites offered adequate cancer-related PA information. All sites reviewed within this study offered PA information for cancer prevention and cancer survivorship. However, while 81% of the sites presented information for SB and cancer prevention, very little information was presented for SB and cancer survivorship, with only 18.2% of the information being offered. Conclusions: The quality and accuracy of cancer-related PA and SB information presented on leading cancer websites is variable. Further information is warranted in the areas of SB, resistance training, and behavior change strategies. Websites have considerable value as knowledge translation tools and, therefore, presenting evidence-based information that is easy to understand may positively impact the health and behaviours of cancer populations, as well as the general population.
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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 | 0.047 | 0.276 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".