Using Facebook to Improve Participation in Colorectal Cancer Screening: Protocol for a Cluster Randomized Controlled Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Colorectal cancer (CRC) screening participation in Canada is lower than the national target, and interventions designed to increase screening participation are generally expensive and have limited impact. Social media can be used as an innovative strategy to increase participation in cancer screening, particularly Facebook (FB), as it is the most popular social media platform for the population eligible for CRC screening. OBJECTIVE The aim of this study is to report on the protocol for a pragmatic cluster randomized controlled trial that will test the effectiveness of CRC social media advertisements on user engagement and screening intention. METHODS The trial will target FB users aged 45 to 64 years who reside in the province of Ontario, Canada. There are 521 forward sortation areas (FSAs) in Ontario, and the randomization will be done at this level using the first 3 digits of the postal code. Rural and urban FSAs will be randomly allocated to one of the 6 study arms. In 4 arms, FB users will all receive one of 4 social media advertisements developed in previous studies, while in the fifth arm, a tailored strategy by sex will be tested. In the final arm, FB users will not be shown any advertisements. If users click on any of the advertisements, they will be directed to a webpage with more information on screening and a place to pledge their intention to screen for CRC. The study’s primary outcome will be tested as a count measure, defined as the number of people per FSA who pledge their intention to screen for CRC. User engagement metrics, including impressions, link clicks, cost per link click, link click-through rate, and user comments, will be tracked across the 5 trial arms with advertisements shown. RESULTS This cluster randomized controlled trial will provide evidence on the use of FB as a tool for delivering CRC screening messages and influencing screening intentions. The comparison of message types within a fixed campaign budget could identify which approaches promote user engagement in both urban and rural populations. CONCLUSIONS This study has the potential to show that social media offers a cost-efficient, scalable approach to promote CRC screening. This approach is adaptable to other cancer screening programs and could provide evidence-based digital strategies for the promotion of cancer screening. CLINICALTRIAL ClinicalTrials.gov NCT04296630; https://tinyurl.com/5y5k7yjb INTERNATIONAL REGISTERED REPORT DERR1-10.2196/86829
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.052 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.161 | 0.026 |
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".