A randomized trial of mail and email recruitment strategies for a physician survey on clinical trial accrual
Bibliographic record
Abstract
BACKGROUND: Patient participation in cancer clinical trials is suboptimal. A challenge to capturing physicians' insights about trials has been low response to surveys. We conducted a study using varying combinations of mail and email to recruit a nationally representative sample of medical, surgical, and radiation oncologists to complete a survey on trial accrual. METHODS: We randomly assigned eligible physicians identified from the American Medical Association MasterFile (n = 13,251) to mail- or email-based recruitment strategies. Mail-based recruitment included a survey packet with: (1) cover letter describing the survey and inviting participation; (2) paper copy of the survey and postage-paid return envelope; and (3) a web link for completing the survey online. Email-based recruitment included an e-mail describing the survey and inviting participation, along with the web link to the survey, and a reminder postcard 2 weeks later. RESULTS: Response was higher for mail-based (11.8, 95% CI 11.0-12.6%) vs. email-based (4.5, 95% CI 4.0-5.0%) recruitment. In email-based recruitment, only one-quarter of recipients opened the email, and even fewer clicked on the link to complete the survey. Most physicians in mail-based recruitment responded after the first invitation (362 of 770 responders, 47.0%). A higher proportion of responders vs. non-responders were young (ages 25-44 years), men, and radiation or surgical (vs. medical) oncologists. CONCLUSIONS: Most physicians assigned to mail-based recruitment actually completed the survey online via the link provided in the cover letter, and those in email-based recruitment did not respond until they received a reminder postcard by mail. Providing the option to return a paper survey or complete it online may have further increased participation in the mail-based group, and future studies should examine how combinations of delivery mode and return options affect physicians' response to surveys.
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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 | 0.055 | 0.094 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 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".