Knowledge of Clinical Trials Among US Cancer Survivors: Cross-Sectional Study of HINTS-SEER Data
Bibliographic record
Abstract
Background: Clinical trials are important for all stages of the cancer control continuum, including cancer survivorship. Objective: The purpose of this study was to evaluate correlates of general clinical trial knowledge among US adult cancer survivors. Methods: We conducted a cross-sectional analysis of the National Cancer Institute's 2021 Health Information National Trends Survey. Cancer survivors were recruited from 3 Surveillance, Epidemiology, and End Results registries: Iowa Cancer Registry, Greater Bay Area Cancer Registry, and New Mexico Tumor Registry. Data collection occurred from January 11 to August 20, 2021. Eligible participants had a cancer diagnosis prior to 2018. The primary outcome was self-reported knowledge of clinical trials, assessed by the question: "How would you describe your level of knowledge about clinical trials?" Responses were dichotomized as knowing "a lot" or "a little bit" versus "don't know anything." Independent variables included sociodemographic characteristics, patient-centered communication, health information seeking (including watching health-related videos on YouTube), and confidence in obtaining cancer-related information. We used survey-weighted logistic regression to examine univariable and multivariable associations with clinical trial knowledge. A total of 2 a priori hypotheses were specified: (1) cancer survivors with a higher perceived quality of patient-centered communication would have greater knowledge of clinical trials than those with a lower perceived quality of patient-centered communication and (2) cancer survivors who were "completely confident" in their ability to obtain cancer-related information would have greater knowledge of clinical trials than those less confident. Odds ratios (ORs), 95% CIs, and P values were estimated using SAS (version 9.4; SAS Institute Inc, Cary, NC, USA). Results: Among cancer survivors (N=1207) included in the analysis, 269 (22.3%) reported that they did not know anything about clinical trials, while 938 (77.7%) reported knowing "a lot" or "a little." Neither of the 2 a priori hypotheses was supported. In the multivariable weighted logistic regression model, greater knowledge of clinical trials was significantly associated with non-Hispanic White race compared with all other races (OR 2.55, 95% CI 1.59, 4.08; P<.001), having a college degree compared with less than a college degree (OR 3.50, 95% CI 2.25, 5.46; P<.001), seeking cancer information from any source (OR 3.04, 95% CI 2.10-4.40; P<.001) compared with not, and ever watched health-related videos on YouTube (OR 2.71, 95% CI 1.49-4.94; P=.002) compared with never watched. In contrast, female sex assigned at birth was associated with lower odds of clinical trial knowledge compared with male sex assigned at birth (OR 0.57, 95% CI 0.41-0.80; P<.001). Conclusions: Sociodemographic characteristics and health-seeking behaviors including watching health-related videos on YouTube were associated with clinical trial knowledge among cancer survivors. These findings highlight opportunities to leverage YouTube as a platform to promote clinical trial awareness and to strengthen survivors' cancer-specific information-seeking skills to improve access to clinical trial information.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".