Remote Participant Recruitment for Pediatric Research During the COVID-19 Pandemic
Bibliographic record
Abstract
Background The COVID-19 pandemic exposed significant vulnerabilities of traditional in-person recruitment methodology in the context of limited access to clinical facilities. Remote recruitment is a potential solution, but its yield and efficiency are unknown. Objective This study aimed to determine remote recruitment and enrollment rates for a pilot feasibility trial of an electronic monitoring device (EMD) for asthma in the pediatric population. Methods Children aged 4-18 years with persistent asthma receiving inhaler medications compatible with an EMD were screened for enrollment in a feasibility and acceptability trial. The emergency department (ED) and inpatient wards were identified as initial in-person recruitment locations prior to the pandemic. Owing to the COVID-19 pandemic, recruitment sites transitioned from exclusive ED or inpatient enrollment to outpatient primary care or pulmonary clinics in an attempt to increase enrollment rates. Study staff called families to determine their interest in the study. Patient age, race and ethnicity, insurance, contact attempts, and reasons for enrollment or refusal were recorded. e-Consent was obtained through the REDCap database, and baseline surveys were administered by telephone. Results Since November 2019, the study staff reached 147 out of 278 (52.3%) eligible families by telephone. In total, 37 (13%) families contacted were enrolled in the study. It took the study staff a mean of 2 attempts to reach individuals for initial enrollment but a mean of 4 additional attempts to complete consent forms. Of the families approached, 47% were Hispanic or Latino, 26.5% were Black or African American, 24.5% were White, and 2% were Asian. Among patients approached, 20% Asian, 16% White, 14.5% Hispanic or Latino, and 12% Black patients were enrolled in the study. Conclusions Telephone recruitment had a low yield across all racial and ethnic groups, averaging approximately 1 successful enrollment per 8 candidates approached. A substantial number of contacts was required to obtain e-consent forms and complete survey questionnaires after participants agreement to enroll. The study findings suggest that when there are barriers to in-person recruitment, remote recruitment is a feasible alternative, but the yield is relatively low, and enrollment requires persistent, repeated follow-up contact. Conflicts of Interest None declared.
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 | 0.115 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".