Can patient decision aids help people make good decisions about participating in clinical trials? A study protocol
Bibliographic record
Abstract
BACKGROUND: Evidence shows that the standard process for obtaining informed consent in clinical trials can be inadequate, with study participants frequently not understanding even basic information fundamental to giving informed consent. Patient decision aids are effective decision support tools originally designed to help patients make difficult treatment or screening decisions. We propose that incorporating decision aids into the informed consent process will improve the extent to which participants make decisions that are informed and consistent with their preferences. A mixed methods study will test this proposal. METHODS: Phase one of this project will involve assessment of a stratified random sample of 50 consent documents from recently completed investigator-initiated clinical trials, according to existing standards for supporting good decision making. Phase two will involve interviews of a purposive sample of 50 trial participants (10 participants from each of five different clinical areas) about their experience of the informed consent process, and how it could be improved. In phase three, we will convert consent forms for two completed clinical trials into decision aids and pilot test these new tools using a user-centered design approach, an iterative development process commonly employed in computer usability literature. In phase four, we will conduct a pilot observational study comparing the new tools to standard consent forms, with potential recruits to two hypothetical clinical trials. Outcomes will include knowledge of key aspects of the decision, knowledge of the probabilities of different outcomes, decisional conflict, the hypothetical participation decision, and qualitative impressions of the experience. DISCUSSION: This work will provide initial evidence about whether a patient decision aid can improve the informed consent process. The larger goal of this work is to examine whether study recruitment can be improved from (barely) informed consent based on disclosure-oriented documents, towards a process of high-quality participant decision-making.
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 | 0.268 | 0.222 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.042 | 0.013 |
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".