Quantifying Canadians' use of the Internet as a source of information on behavioural risk factor modifications related to cancer prevention
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to quantify the frequency and timing of Canadians' Internet searches for information on modifying cancer prevention-related behavioural risk factors. METHODS: We used the Google AdWords Keyword tool to estimate the number of Internet searches in Canada from July 2010 to May 2011 for content associated with the keywords "physical activity / exercise," "healthy eating / weight loss" and "quit smoking." RESULTS: For "physical activity / exercise," 663 related keywords resulted in 117 951 699 searches. For "healthy eating / weight loss," 687 related search terms yielded 98 277 954 searches. "Quit smoking" was associated with 759 related keywords with 31 688 973 searches. All search patterns noticeably peaked in January 2011. CONCLUSION: Many Canadians are actively searching for information on the Internet to support health behaviour change associated with cancer prevention, especially during the month of January. To take advantage of this opportunity, key stakeholders in cancer prevention need to identify knowledge translation priorities and work with health agencies to develop evidence-based strategies to support Internet-facilitated behaviour change.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".