Patient and caregiver engagement in research: factors that influence co-enrollment in research
Bibliographic record
Abstract
BACKGROUND: Recruitment of pediatric participants in studies is difficult due to the vulnerability of this population and the scarcity of certain conditions. Co-enrolling in multiple studies is a strategy that may help overcome this problem. Although anecdotal evidence suggests that co-enrollment may increase patient and caregiver burden, few studies have been conducted from the patient perspective. The objective of this quality improvement project was to elicit patient and caregiver opinions on co-enrolling in multiple research studies. METHODS: Patients and caregivers attending the rheumatology clinic at The Hospital for Sick Children were invited to participate in a semi-structured interview or focus group session. Participants were asked to respond to ten prompts, organized into five categories: experience in clinical research, multiple studies, study selection, study timing and other comments. Sessions were recorded, transcribed and analyzed using NVivo 10 to identify common themes. RESULTS: Overall, eighteen caregivers and two patients were included in the study. Participants felt that the level of study involvement, rather than the number of studies, was the biggest factor affecting their decision to participate. Another factor commonly identified was the competing demands of participants' work and family life. Participants indicated that they generally preferred to be informed about all study opportunities and liked to receive this information prior to their appointments. Once informed, they preferred to be approached by the research team while they were waiting for their appointment. CONCLUSION: Patients and caregivers are open to the concept of co-enrolling in multiple research studies. There are multiple factors which influence decisions to co-enroll in studies including the demands of the study and personal limitations. These findings will help guide the design and practices of future research.
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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 | 0.122 | 0.298 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".