Evaluation of an online knowledge translation intervention to promote cancer risk reduction behaviours: findings from a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Many cancers are preventable through lifestyle modification; however, few adults engage in behaviors that are in line with cancer prevention guidelines. This may be partly due to the mixed messages on effective cancer prevention strategies in popular media. The goal of the McMaster Optimal Aging Portal (the Portal) is to increase access to trustworthy health information. The purpose of this study was to explore if and how knowledge translation strategies to disseminate cancer prevention evidence using the Portal influence participants' knowledge, intentions and health behaviors related to cancer risk. METHODS: Adults ≥40 years old, with no cancer history were randomized to a 12-week intervention (weekly emails and social media posts) or control group. Quantitative data on knowledge, intentions and behaviors (physical activity, diet, alcohol consumption and use of tobacco products) were collected at baseline, end of study and 3 months later. Participant engagement was assessed using Google Analytics, and participant satisfaction through open-ended survey questions and semi-structured interviews. RESULTS: Participants (n = 557, mean age 64.9) were predominantly retired (72%) females (81%). Knowledge of cancer prevention guidelines was higher in the intervention group at end of study only (+ 0.3, p = 0.01). Intentions to follow cancer prevention guidelines increased in both groups, with no between-group differences. Intervention participants reported greater light-intensity physical activity at end of study (+ 0.7 vs. 0.1, p = 0.03), and reduced alcohol intake at follow u (- 0.2 vs. + 0.3, p < 0.05), but no other between-group differences were found. Overall satisfaction with the Portal and intervention materials was high. CONCLUSIONS: Dissemination of evidence-based cancer prevention information through the Portal results in small increases in knowledge of risk-reduction strategies and with little to no impact on self-reported health behaviours, except in particular groups. Further tailoring of knowledge translation strategies may be needed to see more meaningful change in knowledge and health behaviours. TRIAL REGISTRATION: ClinicalTrials.gov NCT03186703, June 14, 2017.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".