Overcoming barriers to implementation of patient engagement in clinical trials: feasibility testing of an embedded study
Bibliographic record
Abstract
BACKGROUND: Patient engagement is attracting considerable interest as a potential strategy to improve the conduct of clinical trials, with evidence of significant improvement in research participant recruitment. However, impact on the retention and adherence of clinical trial participants requires further studies. Embedded studies are specific research designs where a secondary study is "embedded" into a larger host study. We aimed to investigate the feasibility of embedding a study of patient partnership in research, within an ongoing multi-center clinical trial on drug treatment. METHODS: We developed and embedded a patient engagement intervention (embedded study) into a phase 3 randomized clinical drug trial (host study). The patient engagement intervention consisted of discussions between host study participants and a patient partner, to improve research participants' experience and retention in the clinical trial. We carried out individual semi-structured interviews with patient partners and other research team members involved in the development and implementation of the embedded study, as well as an analysis of project documents. Data were analyzed using qualitative thematic analysis. RESULTS: Factors impacting feasibility and lessons learned for future embedded studies on engagement science were identified. Barriers that curtailed the implementation of patient engagement into an ongoing clinical trial included: the late integration of the embedded study into the host clinical trial, different visions of patient partnership and its potential benefits, differences in communication style and preferences, a lack of fit between the specific needs of the host study and the proposed engagement model, and an overall sense of burden. Integrating patient partners into the host clinical trial was seen as potentially beneficial in improving the experience of participants in the host clinical trial through experience sharing, providing support for the consent process, and improving knowledge transfer. CONCLUSIONS: This feasibility study offers insights into how contextual factors and decisions made during the design phase can impact the implementation of patient engagement studies embedded in a clinical trial. Findings suggest that knowledge of the clinical trial context (e.g., organizational, administrative, regulatory, ethics) and early collaboration among embedded study and host study teams before initiation of both studies are key conditions for success.
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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 | 0.445 | 0.486 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".