A Digital Decision Support Tool to Enhance Decisional Capacity for Clinical Trial Consent: Design and Development
Bibliographic record
Abstract
BACKGROUND: Challenges in the clinical and research consent process indicate the need to develop tailored, supportive interventions for all individuals, especially those with limited decisional capacity. We developed a tool to enhance shared decision making and the decisional capacity for individuals with fragile X syndrome engaged in the informed consent process for a clinical trial. OBJECTIVE: We describe the design and development process of a tablet-based decision support tool. METHODS: Our development process for the decision support tool employed a user-centered, feature-driven design approach. We began with an environmental scan to catalog relevant mobile apps, and we conducted interviews with people with a diagnosis of fragile X syndrome and clinicians at fragile X syndrome clinics. To develop content for the decision support tool, we extracted key concepts and elements from a real clinical trial consent form and rewrote it using plain-language principles. RESULTS: We used iterative testing to continuously evaluate and revise the decision support tool content. The tool was finalized in 2016 and contained a series of vignettes, quiz questions, and a sorting activity. A randomized controlled trial was then conducted to compare the efficacy of the decision support tool with a standard verbal presentation of material that mimicked typical informed consent practice. CONCLUSIONS: The informed consent process is primed to leverage digital health resources that promote increased understanding and engagement of research participants in the consent and research process. The process and experiences we describe may provide a model for other digital health design and development initiatives seeking to create more interactive and accessible decision support resources. TRIAL REGISTRATION: ClinicalTrials.gov NCT02465931; https://www.clinicaltrials.gov/ct2/show/NCT02465931 (Archived by WebCite at http://www.webcitation.org/6zx2KY9YW).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.071 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".