Health Literacy and Clinical Trial Participation in French Cancer Patients: A National Survey
Bibliographic record
Abstract
Few studies have explored the relationship between health literacy (HL) and trial participation. In this context, we aimed to study this relationship in French cancer patients. We used data from the French national VIe après le CANcer (VICAN) survey. Two questionnaire items focused on previous invitations to participate in clinical trials and subsequent enrollment. The Single Item Literacy Screener was used to measure functional HL. In total, 1954 cancer patients responded to both VICAN surveys (two and five years after diagnosis). Mean age was 54.1 ± 12.7 years at diagnosis, and 37.6% were classified as having limited HL. One in ten (10.3%) respondents reported having been previously invited to participate in a clinical trial. Of these, 75.5% had enrolled. Limited HL was associated with fewer trial invitations but not with enrollment once invited. Multivariate analysis confirmed the negative effect of limited HL on clinical trial invitation (adjOR = 0.55 (0.39 to 0.77), p < 0.001) after adjustment for multiple characteristics. Patients with limited HL received fewer invitations to participate in trials but were likely to enroll when asked. Addressing HL is necessary to create a more inclusive health system and to reduce inequalities not only in access to innovative cancer care, but to health inequalities in general.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".