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Record W4416538756 · doi:10.2196/76187

Knowledge of Clinical Trials Among US Cancer Survivors: Cross-Sectional Study of HINTS-SEER Data

2025· article· en· W4416538756 on OpenAlexvenueno aff
Aisha T. Langford, Katrina R. Ellis, Nancy Buderer

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsClinical trialLeverage (statistics)CancerCancer treatmentAlternative medicineMEDLINE

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.498
GPT teacher head0.702
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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