Protocol for #iBeatCRC: a community-based intervention to increase early-onset colorectal cancer awareness using a sequential explanatory mixed-methods approach
Bibliographic record
Abstract
INTRODUCTION: Th last two decades have seen a twofold increase in colorectal cancer (CRC) incidence among individuals under the recommended screening age of 50 years. Although the origin of this early-onset CRC (EOCRC) spike remains unknown, prior studies have reported that EOCRC harbours a distinct molecular and clinical phenotype in younger individuals. The sharp increase in EOCRC incidence rates may be attributable to a complex interplay of factors, including race; lifestyle; and ecological, sociodemographic and geographical factors. However, more research that address psychosocial experiences and accounts for lifestyle-related behaviours before, during and after an EOCRC diagnosis are warranted. This study aims to develop and pilot test a theory-driven, community-based intervention to increase awareness of EOCRC, reduce its associated risk factors and improve early detection among adults aged 18-49 years. METHODS AND ANALYSIS: Guided by the Behaviour Change Wheel, we will use a multistage mixed-methods study design. We will pilot a sequential mixed-methods intervention study as follows: (1) First, we will analyse linked quantitative data from the Utah Cancer Registry and National Cancer Institute Surveillance, Epidemiology and End Results registry, linked to state-wide demographic and vital records in the Utah Population Database to identify EOCRC hotspots in Utah by examining the EOCRC incidence and survival variance explained by personal and county-level factors. (2) Next, we will conduct one-on-one interviews with 20 EOCRC survivors residing in EOCRC hotspots to ascertain psychosocial and lifestyle challenges that accompany an EOCRC diagnosis. (3) Finally, we will consider existing evidence-based approaches, our integrated results (quantitative +qualitative) and community action board input to design a community-based intervention to increase EOCRC awareness that can feasibly be delivered by means of outdoor mass media, and via social media. We will pilot the multicomponent media campaign with a quasiexperimental design among 17 EOCRC hotspot residents and 17 EOCRC 'coldspot' residents. ETHICS AND DISSEMINATION: Ethics approval was obtained from the University of Utah Institutional Review Board (IRB_00138357). Signed informed consent will be obtained from all participants prior to any data collection. Study results will be disseminated through CRC community blogs, targeted infographics, conference presentations at national and international professional conferences and publications in peer-reviewed journals. Final intervention-specific data will be available on reasonable request from the corresponding author. TRIAL REGISTRATION NUMBER: NCT04715074.
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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 | 0.040 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.229 | 0.033 |
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".